code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')
UpperCAmelCase = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'rb' ) as f:
lowercase = Image.open(__SCREAMING_SNAKE_CASE )
return im.convert('RGB' )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={
"""help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
} , )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
_UpperCamelCase : Optional[str] = field(default=__lowerCamelCase , metadata={"""help""": """A folder containing the training data."""} )
_UpperCamelCase : Optional[str] = field(default=__lowerCamelCase , metadata={"""help""": """A folder containing the validation data."""} )
_UpperCamelCase : Optional[float] = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
_UpperCamelCase : Optional[int] = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_UpperCamelCase : Optional[int] = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def SCREAMING_SNAKE_CASE__ ( self ):
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'You must specify either a dataset name from the hub or a train and/or validation directory.' )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : str = field(
default="""google/vit-base-patch16-224-in21k""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__lowerCamelCase )} , )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
_UpperCamelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_UpperCamelCase : str = field(default=__lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = torch.stack([example['pixel_values'] for example in examples] )
lowercase = torch.tensor([example['labels'] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def UpperCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_image_classification' , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(__SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task='image-classification' , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowercase = {}
if data_args.train_dir is not None:
lowercase = os.path.join(data_args.train_dir , '**' )
if data_args.validation_dir is not None:
lowercase = os.path.join(data_args.validation_dir , '**' )
lowercase = load_dataset(
'imagefolder' , data_files=__SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , task='image-classification' , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase = None if 'validation' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __SCREAMING_SNAKE_CASE ) and data_args.train_val_split > 0.0:
lowercase = dataset['train'].train_test_split(data_args.train_val_split )
lowercase = split['train']
lowercase = split['test']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = dataset['train'].features['labels'].names
lowercase , lowercase = {}, {}
for i, label in enumerate(__SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__SCREAMING_SNAKE_CASE ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__SCREAMING_SNAKE_CASE ) , labelaid=__SCREAMING_SNAKE_CASE , idalabel=__SCREAMING_SNAKE_CASE , finetuning_task='image-classification' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
lowercase = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
lowercase = image_processor.size['shortest_edge']
else:
lowercase = (image_processor.size['height'], image_processor.size['width'])
lowercase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
lowercase = Compose(
[
RandomResizedCrop(__SCREAMING_SNAKE_CASE ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
lowercase = Compose(
[
Resize(__SCREAMING_SNAKE_CASE ),
CenterCrop(__SCREAMING_SNAKE_CASE ),
ToTensor(),
normalize,
] )
def train_transforms(__SCREAMING_SNAKE_CASE ):
lowercase = [
_train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']
]
return example_batch
def val_transforms(__SCREAMING_SNAKE_CASE ):
lowercase = [_val_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
lowercase = (
dataset['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(__SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
lowercase = (
dataset['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(__SCREAMING_SNAKE_CASE )
# Initalize our trainer
lowercase = Trainer(
model=__SCREAMING_SNAKE_CASE , args=__SCREAMING_SNAKE_CASE , train_dataset=dataset['train'] if training_args.do_train else None , eval_dataset=dataset['validation'] if training_args.do_eval else None , compute_metrics=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , data_collator=__SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics('eval' , __SCREAMING_SNAKE_CASE )
trainer.save_metrics('eval' , __SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
lowercase = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'image-classification',
'dataset': data_args.dataset_name,
'tags': ['image-classification', 'vision'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = DistilBertModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = DistilBertForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = DistilBertForQuestionAnswering(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=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 SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_labels
lowercase = DistilBertForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_labels
lowercase = DistilBertForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_choices
lowercase = DistilBertForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase = model(
snake_case , attention_mask=snake_case , labels=snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
((lowercase) , (lowercase) , (lowercase) , (lowercase) , (lowercase) , (lowercase)) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
_UpperCamelCase : str = (
{
"""feature-extraction""": DistilBertModel,
"""fill-mask""": DistilBertForMaskedLM,
"""question-answering""": DistilBertForQuestionAnswering,
"""text-classification""": DistilBertForSequenceClassification,
"""token-classification""": DistilBertForTokenClassification,
"""zero-shot""": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : str = True
_UpperCamelCase : List[Any] = True
_UpperCamelCase : Optional[int] = True
_UpperCamelCase : Tuple = True
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = DistilBertModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , dim=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = DistilBertModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowercase = True
lowercase = model_class(config=snake_case )
lowercase = self._prepare_for_class(snake_case , snake_case )
lowercase = torch.jit.trace(
snake_case , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(snake_case , os.path.join(snake_case , 'traced_model.pt' ) )
lowercase = torch.jit.load(os.path.join(snake_case , 'traced_model.pt' ) , map_location=snake_case )
loaded(inputs_dict['input_ids'].to(snake_case ) , inputs_dict['attention_mask'].to(snake_case ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = DistilBertModel.from_pretrained('distilbert-base-uncased' )
lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase = model(snake_case , attention_mask=snake_case )[0]
lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , snake_case )
lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , snake_case , atol=1E-4 ) )
| 84 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=2 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , snake_case=0 , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = projection_dim
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
lowercase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFDPRContextEncoder(config=snake_case )
lowercase = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFDPRQuestionEncoder(config=snake_case )
lowercase = model(snake_case , attention_mask=snake_case , token_type_ids=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = TFDPRReader(config=snake_case )
lowercase = model(snake_case , attention_mask=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) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids}
return config, inputs_dict
@require_tf
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_UpperCamelCase : Union[str, Any] = {"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}
_UpperCamelCase : Any = False
_UpperCamelCase : int = False
_UpperCamelCase : List[str] = False
_UpperCamelCase : str = False
_UpperCamelCase : Union[str, Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFDPRModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = TFDPRContextEncoder.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = TFDPRContextEncoder.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = TFDPRQuestionEncoder.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = TFDPRReader.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' )
lowercase = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]] ) # [CLS] hello, is my dog cute? [SEP]
lowercase = model(snake_case )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
lowercase = tf.constant(
[
[
0.03_236_253,
0.12_753_335,
0.16_818_509,
0.00_279_786,
0.3_896_933,
0.24_264_945,
0.2_178_971,
-0.02_335_227,
-0.08_481_959,
-0.14_324_117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''ReformerTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''ReformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ReformerAttention''',
'''ReformerForMaskedLM''',
'''ReformerForQuestionAnswering''',
'''ReformerForSequenceClassification''',
'''ReformerLayer''',
'''ReformerModel''',
'''ReformerModelWithLMHead''',
'''ReformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 |
import unittest
from transformers import 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 (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTDoubleHeadsModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = self.num_labels
lowercase = OpenAIGPTForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCamelCase : str = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=False ):
lowercase = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=snake_case , )
lowercase = inputs_dict['labels']
lowercase = inputs_dict['labels']
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=snake_case , )
lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = OpenAIGPTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(snake_case )
lowercase = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=snake_case ) # the president is
lowercase = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 84 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ): # picklable for multiprocessing
return x.sum()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ): # picklable for multiprocessing
return i + 1
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : str
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = {}
lowercase = []
lowercase = 1
lowercase = [1, 2]
lowercase = {'a': 1, 'b': 2}
lowercase = {'a': [1, 2], 'b': [3, 4]}
lowercase = {'a': {'1': 1}, 'b': 2}
lowercase = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
lowercase = {}
lowercase = []
lowercase = 2
lowercase = [2, 3]
lowercase = {'a': 2, 'b': 3}
lowercase = {'a': [2, 3], 'b': [4, 5]}
lowercase = {'a': {'1': 2}, 'b': 3}
lowercase = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case ) , snake_case )
lowercase = 2
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(map_nested(snake_case , snake_case , num_proc=snake_case ) , snake_case )
lowercase = {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
lowercase = {'a': 2, 'b': 0, 'c': 2}
lowercase = {
'a': np.eye(2 ).astype(snake_case ),
'b': np.zeros(3 ).astype(snake_case ),
'c': np.ones(2 ).astype(snake_case ),
}
self.assertEqual(map_nested(snake_case , snake_case , map_numpy=snake_case ) , snake_case )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(snake_case , snake_case , map_numpy=snake_case ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(snake_case , snake_case , map_numpy=snake_case , num_proc=snake_case ) , snake_case )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(snake_case , snake_case , map_numpy=snake_case , num_proc=snake_case ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(snake_case ): # can't pickle a local lambda
map_nested(lambda snake_case : x + 1 , snake_case , num_proc=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = {'a': 1, 'b': 2}
lowercase = {'a': 3, 'b': 4}
lowercase = {'a': 5, 'b': 6}
lowercase = sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(snake_case , snake_case , snake_case ) ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
class A_ :
'''simple docstring'''
_UpperCamelCase : Tuple = """bar"""
lowercase = Foo()
self.assertEqual(foo.my_attr , 'bar' )
with temporary_assignment(snake_case , 'my_attr' , 'BAR' ):
self.assertEqual(foo.my_attr , 'BAR' )
self.assertEqual(foo.my_attr , 'bar' )
@pytest.mark.parametrize(
'iterable_length, num_proc, expected_num_proc' , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
lowercase = {F'''{i}''': i for i in range(__SCREAMING_SNAKE_CASE )}
lowercase = map_nested(lambda __SCREAMING_SNAKE_CASE : x + 10 , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class A_ ( __lowerCamelCase ):
'''simple docstring'''
@require_tf
def SCREAMING_SNAKE_CASE__ ( self ):
import tensorflow as tf
from tensorflow.keras import layers
lowercase = layers.Dense(2 )
def gen_random_output():
lowercase = tf.random.uniform((1, 3) )
return model(snake_case ).numpy()
with temp_seed(42 , set_tensorflow=snake_case ):
lowercase = gen_random_output()
with temp_seed(42 , set_tensorflow=snake_case ):
lowercase = gen_random_output()
lowercase = gen_random_output()
np.testing.assert_equal(snake_case , snake_case )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self ):
import torch
def gen_random_output():
lowercase = torch.nn.Linear(3 , 2 )
lowercase = torch.rand(1 , 3 )
return model(snake_case ).detach().numpy()
with temp_seed(42 , set_pytorch=snake_case ):
lowercase = gen_random_output()
with temp_seed(42 , set_pytorch=snake_case ):
lowercase = gen_random_output()
lowercase = gen_random_output()
np.testing.assert_equal(snake_case , snake_case )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def SCREAMING_SNAKE_CASE__ ( self ):
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
lowercase = gen_random_output()
with temp_seed(42 ):
lowercase = gen_random_output()
lowercase = gen_random_output()
np.testing.assert_equal(snake_case , snake_case )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize('input_data' , [{}] )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = NestedDataStructure(__SCREAMING_SNAKE_CASE ).data
assert output_data == input_data
@pytest.mark.parametrize(
'data, expected_output' , [
({}, []),
([], []),
('foo', ['foo']),
(['foo', 'bar'], ['foo', 'bar']),
([['foo', 'bar']], ['foo', 'bar']),
([[['foo'], ['bar']]], ['foo', 'bar']),
([[['foo'], 'bar']], ['foo', 'bar']),
({'a': 1, 'b': 2}, [1, 2]),
({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
({'a': {'1': 1}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
] , )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = NestedDataStructure(__SCREAMING_SNAKE_CASE ).flatten()
assert output == expected_output
def UpperCAmelCase_ ( ):
lowercase = A(x=1 , y='foobar' )
lowercase = {'x': 1, 'y': 'foobar'}
assert asdict(__SCREAMING_SNAKE_CASE ) == expected_output
lowercase = {'a': {'b': A(x=10 , y='foo' )}, 'c': [A(x=20 , y='bar' )]}
lowercase = {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
assert asdict(__SCREAMING_SNAKE_CASE ) == expected_output
with pytest.raises(__SCREAMING_SNAKE_CASE ):
asdict([1, A(x=10 , y='foo' )] )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
return text.split()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def UpperCAmelCase_ ( ):
with Pool(2 ) as pool:
lowercase = list(iflatmap_unordered(__SCREAMING_SNAKE_CASE , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(__SCREAMING_SNAKE_CASE ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
lowercase = list(iflatmap_unordered(__SCREAMING_SNAKE_CASE , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(__SCREAMING_SNAKE_CASE ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
lowercase = []
for yield_time, content in iflatmap_unordered(
__SCREAMING_SNAKE_CASE , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(__SCREAMING_SNAKE_CASE )
assert out.count('a' ) == 2
assert out.count('b' ) == 2
assert len(__SCREAMING_SNAKE_CASE ) == 4
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
lowercase = 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()
| 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
lowercase = False
lowercase = index + i
lowercase = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 9999_6666_3333 ):
lowercase = math.floor(math.sqrt(__SCREAMING_SNAKE_CASE ) ) + 100
lowercase = prime_sieve(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = primes[prime_index]
while (last_prime**2) <= limit:
lowercase = primes[prime_index + 1]
lowercase = last_prime**2
lowercase = next_prime**2
# Get numbers divisible by lps(current)
lowercase = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowercase = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowercase = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowercase = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 84 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
UpperCAmelCase = logging.getLogger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return (preds == labels).mean()
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : str = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
_UpperCamelCase : str = field(metadata={"""help""": """Should contain the data files for the task."""} )
_UpperCamelCase : int = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def UpperCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
' --overwrite_output_dir to overcome.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , __SCREAMING_SNAKE_CASE )
# Set seed
set_seed(training_args.seed )
try:
lowercase = processors[data_args.task_name]()
lowercase = processor.get_labels()
lowercase = len(__SCREAMING_SNAKE_CASE )
except KeyError:
raise ValueError('Task not found: %s' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__SCREAMING_SNAKE_CASE , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
lowercase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowercase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__SCREAMING_SNAKE_CASE , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=__SCREAMING_SNAKE_CASE , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(__SCREAMING_SNAKE_CASE ) -> Dict:
lowercase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE , p.label_ids )}
# Data collator
lowercase = DataCollatorWithPadding(__SCREAMING_SNAKE_CASE , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase = Trainer(
model=__SCREAMING_SNAKE_CASE , args=__SCREAMING_SNAKE_CASE , train_dataset=__SCREAMING_SNAKE_CASE , eval_dataset=__SCREAMING_SNAKE_CASE , compute_metrics=__SCREAMING_SNAKE_CASE , data_collator=__SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowercase = trainer.evaluate()
lowercase = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_master():
with open(__SCREAMING_SNAKE_CASE , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
writer.write('%s = %s\n' % (key, value) )
results.update(__SCREAMING_SNAKE_CASE )
return results
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
UpperCAmelCase = re.compile(R'''^\s*try:''')
# Catches a line with else:
UpperCAmelCase = re.compile(R'''^\s*else:''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if _re_test_backend.search(__SCREAMING_SNAKE_CASE ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ):
lowercase = _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ).groups()[0]
lowercase = re.findall(r'\[([^\]]+)\]' , __SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_between_brackets.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
def find_duplicates(__SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def UpperCAmelCase_ ( ):
lowercase = []
for root, _, files in os.walk(__SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' )
lowercase = parse_init(__SCREAMING_SNAKE_CASE )
if objects is not None:
lowercase = analyze_results(*__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = []
for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / folder).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace(os.path.sep , '.' )
submodules.append(__SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / fname).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__SCREAMING_SNAKE_CASE )
return submodules
UpperCAmelCase = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def UpperCAmelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
lowercase = direct_transformers_import(__SCREAMING_SNAKE_CASE )
lowercase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
lowercase = f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , __SCREAMING_SNAKE_CASE ) ) )
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 84 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
UpperCAmelCase = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
'''models/graphormer/algos_graphormer.pyx''',
]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# Test all the extensions added in the setup
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
UpperCAmelCase = parser.parse_args()
if args.check_lib:
UpperCAmelCase = importlib.import_module('''transformers''')
UpperCAmelCase = Path(transformers_module.__file__).parent
else:
UpperCAmelCase = Path.cwd() / '''build/lib/transformers'''
if not test_custom_files_are_present(transformers_path):
raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
| 84 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = None
def __str__( self ):
return F'''{self.data}'''
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
lowercase = None
def __iter__( self ):
lowercase = self.top
while node:
yield node.data
lowercase = node.next
def __str__( self ):
return "->".join([str(snake_case ) for item in self] )
def __len__( self ):
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.top is None
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = Node(snake_case )
if not self.is_empty():
lowercase = self.top
lowercase = node
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case )
lowercase = self.top
lowercase = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = str(id_ )
lowercase = None
lowercase = None
lowercase = []
lowercase = {} # {vertex:distance}
def __lt__( self , snake_case ):
return self.key < other.key
def __repr__( self ):
return self.id
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
self.neighbors.append(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = weight
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __SCREAMING_SNAKE_CASE )
graph[b - 1].add_edge(graph[a - 1] , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for u in graph:
lowercase = math.inf
lowercase = None
lowercase = 0
lowercase = graph[:]
while q:
lowercase = min(__SCREAMING_SNAKE_CASE )
q.remove(__SCREAMING_SNAKE_CASE )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
lowercase = u
lowercase = u.edges[v.id]
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for u in graph:
lowercase = math.inf
lowercase = None
lowercase = 0
lowercase = list(__SCREAMING_SNAKE_CASE )
hq.heapify(__SCREAMING_SNAKE_CASE )
while h:
lowercase = hq.heappop(__SCREAMING_SNAKE_CASE )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
lowercase = u
lowercase = u.edges[v.id]
hq.heapify(__SCREAMING_SNAKE_CASE )
for i in range(1 , len(__SCREAMING_SNAKE_CASE ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
# For consistency across different places the DisjunctiveConstraint is called,
# dc.token_ids is a list of integers. It is also initialized only by integers.
lowercase = [[1, 2, 4], [1, 2, 3, 4]]
lowercase = DisjunctiveConstraint(snake_case )
self.assertTrue(isinstance(dc.token_ids , snake_case ) )
with self.assertRaises(snake_case ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(snake_case ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def SCREAMING_SNAKE_CASE__ ( self ):
# We can't have constraints that are complete subsets of another. This leads to a preverse
# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
lowercase = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(snake_case ):
DisjunctiveConstraint(snake_case ) # fails here
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [[1, 2, 3], [1, 2, 4]]
lowercase = DisjunctiveConstraint(snake_case )
lowercase , lowercase , lowercase = dc.update(1 )
lowercase = stepped is True and completed is False and reset is False
self.assertTrue(snake_case )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
lowercase = stepped is True and completed is False and reset is False
self.assertTrue(snake_case )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(3 )
lowercase = stepped is True and completed is True and reset is False
self.assertTrue(snake_case )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
lowercase = DisjunctiveConstraint(snake_case )
lowercase , lowercase , lowercase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
lowercase , lowercase , lowercase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
lowercase , lowercase , lowercase = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
lowercase , lowercase , lowercase = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
lowercase , lowercase , lowercase = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 84 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : Dict = """dummy_data"""
_UpperCamelCase : Optional[int] = """datasets"""
_UpperCamelCase : Tuple = False
def __init__( self , snake_case , snake_case , snake_case , snake_case = None , snake_case = False , snake_case = True , snake_case = None , ):
lowercase = 0
lowercase = dataset_name
lowercase = cache_dir
lowercase = use_local_dummy_data
lowercase = config
# download_callbacks take a single url as input
lowercase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase = str(snake_case )
# to be downloaded
lowercase = None
lowercase = None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._dummy_file is None:
lowercase = self.download_dummy_data()
return self._dummy_file
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase = cached_path(
snake_case , cache_dir=self.cache_dir , extract_compressed_file=snake_case , force_extract=snake_case )
return os.path.join(snake_case , self.dummy_file_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._bucket_url is None:
lowercase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def SCREAMING_SNAKE_CASE__ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case , snake_case ):
return self.create_dummy_data_dict(snake_case , snake_case )
elif isinstance(snake_case , (list, tuple) ):
return self.create_dummy_data_list(snake_case , snake_case )
else:
return self.create_dummy_data_single(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case , **snake_case ):
return path
def SCREAMING_SNAKE_CASE__ ( self ):
return {}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case , snake_case ):
for single_url in single_urls:
download_callback(snake_case )
else:
lowercase = single_urls
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(snake_case , snake_case ):
lowercase = [os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) ) for x in single_urls]
else:
lowercase = single_urls
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) )
lowercase = value
# make sure that values are unique
if all(isinstance(snake_case , snake_case ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , snake_case ) ) for url in data_url )
lowercase = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase = [data_url[0]] * len(snake_case )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(snake_case )
return dummy_data_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(snake_case ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
def _iter_archive_members(snake_case ):
# this preserves the order of the members inside the ZIP archive
lowercase = Path(self.dummy_file ).parent
lowercase = path.relative_to(snake_case )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case )
lowercase = Path(snake_case )
lowercase = _iter_archive_members(snake_case ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(snake_case ).as_posix(), file_path.open('rb' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
lowercase = [paths]
for path in paths:
if os.path.isfile(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(snake_case ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(snake_case , snake_case )
| 84 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('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 SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError('only integers accepted as input' )
else:
lowercase = str(abs(__SCREAMING_SNAKE_CASE ) )
lowercase = [list(__SCREAMING_SNAKE_CASE ) for char in range(len(__SCREAMING_SNAKE_CASE ) )]
for index in range(len(__SCREAMING_SNAKE_CASE ) ):
num_transpositions[index].pop(__SCREAMING_SNAKE_CASE )
return max(
int(''.join(list(__SCREAMING_SNAKE_CASE ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 84 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_terminal_summary_main
lowercase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE , id=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase = 0
# Doctest custom flag to ignore output.
UpperCAmelCase = doctest.register_optionflag('''IGNORE_RESULT''')
UpperCAmelCase = doctest.OutputChecker
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , snake_case , snake_case , snake_case )
UpperCAmelCase = CustomOutputChecker
UpperCAmelCase = HfDoctestModule
UpperCAmelCase = HfDocTestParser
| 84 | 1 |
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """conditional_detr"""
_UpperCamelCase : Any = ["""past_key_values"""]
_UpperCamelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , snake_case=True , snake_case=None , snake_case=3 , snake_case=300 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case="relu" , snake_case=256 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1.0 , snake_case=False , snake_case="sine" , snake_case="resnet50" , snake_case=True , snake_case=False , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=1 , snake_case=1 , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=0.25 , **snake_case , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case , snake_case ):
lowercase = backbone_config.get('model_type' )
lowercase = CONFIG_MAPPING[backbone_model_type]
lowercase = config_class.from_dict(snake_case )
lowercase = use_timm_backbone
lowercase = backbone_config
lowercase = num_channels
lowercase = num_queries
lowercase = d_model
lowercase = encoder_ffn_dim
lowercase = encoder_layers
lowercase = encoder_attention_heads
lowercase = decoder_ffn_dim
lowercase = decoder_layers
lowercase = decoder_attention_heads
lowercase = dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = activation_function
lowercase = init_std
lowercase = init_xavier_std
lowercase = encoder_layerdrop
lowercase = decoder_layerdrop
lowercase = encoder_layers
lowercase = auxiliary_loss
lowercase = position_embedding_type
lowercase = backbone
lowercase = use_pretrained_backbone
lowercase = dilation
# Hungarian matcher
lowercase = class_cost
lowercase = bbox_cost
lowercase = giou_cost
# Loss coefficients
lowercase = mask_loss_coefficient
lowercase = dice_loss_coefficient
lowercase = cls_loss_coefficient
lowercase = bbox_loss_coefficient
lowercase = giou_loss_coefficient
lowercase = focal_alpha
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.d_model
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 12
| 84 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase = n_token
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [n_token]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowercase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowercase = nn.Parameter(torch.zeros(self.n_clusters ) )
lowercase = nn.ModuleList()
lowercase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
else:
self.out_projs.append(snake_case )
self.out_layers.append(nn.Linear(snake_case , snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
self.out_layers.append(nn.Linear(snake_case , r_idx - l_idx ) )
lowercase = keep_order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if proj is None:
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowercase = nn.functional.linear(snake_case , proj.t().contiguous() )
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None , snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
lowercase = hidden[..., :-1, :].contiguous()
lowercase = labels[..., 1:].contiguous()
lowercase = hidden.view(-1 , hidden.size(-1 ) )
lowercase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
lowercase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowercase = labels != -100
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = (
-nn.functional.log_softmax(snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowercase = nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
if labels is None:
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = 0
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowercase = (labels >= l_idx) & (labels < r_idx)
lowercase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowercase = labels.index_select(0 , snake_case ) - l_idx
lowercase = head_logprob.index_select(0 , snake_case )
lowercase = hidden.index_select(0 , snake_case )
else:
lowercase = hidden
if i == 0:
if labels is not None:
lowercase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowercase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowercase = logprob_i
if labels is not None:
if (hasattr(self , 'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 , snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = head_logprob[:, -i] + tail_logprob_i
lowercase = logprob_i
return out
| 84 | 1 |
import os
def UpperCAmelCase_ ( ):
lowercase = os.path.join(os.path.dirname(__SCREAMING_SNAKE_CASE ) , 'num.txt' )
with open(__SCREAMING_SNAKE_CASE ) as file_hand:
return str(sum(int(__SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 84 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.' )
if len(snake_case ) != 0:
lowercase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(snake_case ) != cols:
raise error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise error
lowercase = rows
else:
lowercase = []
def SCREAMING_SNAKE_CASE__ ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows[0] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return (self.num_rows, self.num_columns)
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.order[0] == self.order[1]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return bool(self.determinant() )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(snake_case ).determinant()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if (row + column) % 2 == 0:
return self.get_minor(snake_case , snake_case )
return -1 * self.get_minor(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[self.get_minor(snake_case , snake_case ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
for row in self.rows
] )
+ "]"
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError('Row must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix' )
if position is None:
self.rows.append(snake_case )
else:
lowercase = self.rows[0:position] + [row] + self.rows[position:]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError(
'Column must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in column:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix' )
if position is None:
lowercase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , snake_case ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , snake_case ):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , snake_case ):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , snake_case ):
if isinstance(snake_case , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(snake_case , snake_case ):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second' )
return Matrix(
[
[Matrix.dot_product(snake_case , snake_case ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix' )
def __pow__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise TypeError('A Matrix can only be raised to the power of an int' )
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power' )
lowercase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case ):
return sum(row[i] * column[i] for i in range(len(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ['''model.decoder.embed_positions.weights''']
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if "emb" in name:
lowercase = name.replace('emb' , 'model.decoder.embed_tokens' )
if "transformer" in name:
lowercase = name.replace('transformer' , 'model.decoder' )
if "cross_attention" in name:
lowercase = name.replace('cross_attention' , 'encoder_attn' )
if "linear1" in name:
lowercase = name.replace('linear1' , 'fc1' )
if "linear2" in name:
lowercase = name.replace('linear2' , 'fc2' )
if "norm1" in name:
lowercase = name.replace('norm1' , 'self_attn_layer_norm' )
if "norm_cross" in name:
lowercase = name.replace('norm_cross' , 'encoder_attn_layer_norm' )
if "norm2" in name:
lowercase = name.replace('norm2' , 'final_layer_norm' )
if "out_norm" in name:
lowercase = name.replace('out_norm' , 'model.decoder.layer_norm' )
if "linears" in name:
lowercase = name.replace('linears' , 'lm_heads' )
if "condition_provider.conditioners.description.output_proj" in name:
lowercase = name.replace('condition_provider.conditioners.description.output_proj' , 'enc_to_dec_proj' )
return name
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = list(state_dict.keys() )
lowercase = {}
for key in keys:
lowercase = state_dict.pop(__SCREAMING_SNAKE_CASE )
lowercase = rename_keys(__SCREAMING_SNAKE_CASE )
if "in_proj_weight" in key:
# split fused qkv proj
lowercase = val[:hidden_size, :]
lowercase = val[hidden_size : 2 * hidden_size, :]
lowercase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
lowercase = val
else:
lowercase = val
return state_dict, enc_dec_proj_state_dict
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if checkpoint == "small":
# default config values
lowercase = 1024
lowercase = 24
lowercase = 16
elif checkpoint == "medium":
lowercase = 1536
lowercase = 48
lowercase = 24
elif checkpoint == "large":
lowercase = 2048
lowercase = 48
lowercase = 32
else:
raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
lowercase = MusicgenDecoderConfig(
hidden_size=__SCREAMING_SNAKE_CASE , ffn_dim=hidden_size * 4 , num_hidden_layers=__SCREAMING_SNAKE_CASE , num_attention_heads=__SCREAMING_SNAKE_CASE , )
return config
@torch.no_grad()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="cpu" ):
lowercase = MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE , device=__SCREAMING_SNAKE_CASE )
lowercase = decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE )
lowercase = fairseq_model.lm.state_dict()
lowercase , lowercase = rename_state_dict(
__SCREAMING_SNAKE_CASE , hidden_size=decoder_config.hidden_size )
lowercase = TaEncoderModel.from_pretrained('t5-base' )
lowercase = EncodecModel.from_pretrained('facebook/encodec_32khz' )
lowercase = MusicgenForCausalLM(__SCREAMING_SNAKE_CASE ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
lowercase , lowercase = decoder.load_state_dict(__SCREAMING_SNAKE_CASE , strict=__SCREAMING_SNAKE_CASE )
for key in missing_keys.copy():
if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}''' )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}''' )
# init the composite model
lowercase = MusicgenForConditionalGeneration(text_encoder=__SCREAMING_SNAKE_CASE , audio_encoder=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__SCREAMING_SNAKE_CASE )
# check we can do a forward pass
lowercase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
lowercase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
lowercase = model(input_ids=__SCREAMING_SNAKE_CASE , decoder_input_ids=__SCREAMING_SNAKE_CASE ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('Incorrect shape for logits' )
# now construct the processor
lowercase = AutoTokenizer.from_pretrained('t5-base' )
lowercase = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' , padding_side='left' )
lowercase = MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
# set the appropriate bos/pad token ids
lowercase = 2048
lowercase = 2048
# set other default generation config params
lowercase = int(30 * audio_encoder.config.frame_rate )
lowercase = True
lowercase = 3.0
if pytorch_dump_folder is not None:
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if repo_id:
logger.info(F'''Pushing model {checkpoint} to {repo_id}''' )
model.push_to_hub(__SCREAMING_SNAKE_CASE )
processor.push_to_hub(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint''',
default='''small''',
type=str,
help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
)
parser.add_argument(
'''--pytorch_dump_folder''',
required=True,
default=None,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
parser.add_argument(
'''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
)
UpperCAmelCase = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 84 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=8 ):
lowercase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , ):
super().__init__()
self.register_modules(
unet=snake_case , scheduler=snake_case , movq=snake_case , )
lowercase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
if latents is None:
lowercase = randn_tensor(snake_case , generator=snake_case , device=snake_case , dtype=snake_case )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase = latents.to(snake_case )
lowercase = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
lowercase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=snake_case )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase , lowercase = cpu_offload_with_hook(snake_case , snake_case , prev_module_hook=snake_case )
# We'll offload the last model manually.
lowercase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE__ ( self ):
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(snake_case , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(snake_case )
def __call__( self , snake_case , snake_case , snake_case = 512 , snake_case = 512 , snake_case = 100 , snake_case = 4.0 , snake_case = 1 , snake_case = None , snake_case = None , snake_case = "pil" , snake_case = True , ):
lowercase = self._execution_device
lowercase = guidance_scale > 1.0
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
lowercase = image_embeds.shape[0] * num_images_per_prompt
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
if do_classifier_free_guidance:
lowercase = image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = negative_image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=snake_case )
self.scheduler.set_timesteps(snake_case , device=snake_case )
lowercase = self.scheduler.timesteps
lowercase = self.unet.config.in_channels
lowercase , lowercase = downscale_height_and_width(snake_case , snake_case , self.movq_scale_factor )
# create initial latent
lowercase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , snake_case , snake_case , snake_case , self.scheduler , )
for i, t in enumerate(self.progress_bar(snake_case ) ):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase = {'image_embeds': image_embeds}
lowercase = self.unet(
sample=snake_case , timestep=snake_case , encoder_hidden_states=snake_case , added_cond_kwargs=snake_case , return_dict=snake_case , )[0]
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
lowercase , lowercase = noise_pred.chunk(2 )
lowercase , lowercase = variance_pred.chunk(2 )
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(
snake_case , snake_case , snake_case , generator=snake_case , )[0]
# post-processing
lowercase = self.movq.decode(snake_case , force_not_quantize=snake_case )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase = image * 0.5 + 0.5
lowercase = image.clamp(0 , 1 )
lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case )
| 84 | 1 |
from collections.abc import Callable
import numpy as np
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = int(np.ceil((x_end - xa) / step_size ) )
lowercase = np.zeros((n + 1,) )
lowercase = ya
lowercase = xa
for k in range(__SCREAMING_SNAKE_CASE ):
lowercase = y[k] + step_size * ode_func(__SCREAMING_SNAKE_CASE , y[k] )
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
UpperCAmelCase = TypeVar('''U''')
class A_ ( Generic[T, U] ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ):
lowercase = key
lowercase = val
lowercase = None
lowercase = None
def __repr__( self ):
return (
F'''Node: key: {self.key}, val: {self.val}, '''
F'''has next: {bool(self.next )}, has prev: {bool(self.prev )}'''
)
class A_ ( Generic[T, U] ):
'''simple docstring'''
def __init__( self ):
lowercase = DoubleLinkedListNode(snake_case , snake_case )
lowercase = DoubleLinkedListNode(snake_case , snake_case )
lowercase , lowercase = self.rear, self.head
def __repr__( self ):
lowercase = ['DoubleLinkedList']
lowercase = self.head
while node.next is not None:
rep.append(str(snake_case ) )
lowercase = node.next
rep.append(str(self.rear ) )
return ",\n ".join(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
lowercase = node
lowercase = previous
lowercase = node
lowercase = self.rear
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if node.prev is None or node.next is None:
return None
lowercase = node.next
lowercase = node.prev
lowercase = None
lowercase = None
return node
class A_ ( Generic[T, U] ):
'''simple docstring'''
_UpperCamelCase : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self , snake_case ):
lowercase = DoubleLinkedList()
lowercase = capacity
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = {}
def __repr__( self ):
return (
F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
F'''capacity={self.capacity}, current size={self.num_keys})'''
)
def __contains__( self , snake_case ):
return key in self.cache
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
lowercase = self.cache[key]
lowercase = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(snake_case )
return node.val
self.miss += 1
return None
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
lowercase = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(snake_case ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
lowercase = DoubleLinkedListNode(snake_case , snake_case )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
lowercase = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
lowercase = value
self.list.add(snake_case )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case = 128 ):
def cache_decorator_inner(snake_case ) -> Callable[..., U]:
def cache_decorator_wrapper(*snake_case ) -> U:
if func not in cls.decorator_function_to_instance_map:
lowercase = LRUCache(snake_case )
lowercase = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
lowercase = func(*snake_case )
cls.decorator_function_to_instance_map[func].put(args[0] , snake_case )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(snake_case , 'cache_info' , snake_case ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCREAMING_SNAKE_CASE ):
if is_palindrome(__SCREAMING_SNAKE_CASE ) and is_palindrome(bin(__SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''ConvNextFeatureExtractor''']
UpperCAmelCase = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 84 |
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """conditional_detr"""
_UpperCamelCase : Any = ["""past_key_values"""]
_UpperCamelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , snake_case=True , snake_case=None , snake_case=3 , snake_case=300 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case="relu" , snake_case=256 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1.0 , snake_case=False , snake_case="sine" , snake_case="resnet50" , snake_case=True , snake_case=False , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=1 , snake_case=1 , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=0.25 , **snake_case , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case , snake_case ):
lowercase = backbone_config.get('model_type' )
lowercase = CONFIG_MAPPING[backbone_model_type]
lowercase = config_class.from_dict(snake_case )
lowercase = use_timm_backbone
lowercase = backbone_config
lowercase = num_channels
lowercase = num_queries
lowercase = d_model
lowercase = encoder_ffn_dim
lowercase = encoder_layers
lowercase = encoder_attention_heads
lowercase = decoder_ffn_dim
lowercase = decoder_layers
lowercase = decoder_attention_heads
lowercase = dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = activation_function
lowercase = init_std
lowercase = init_xavier_std
lowercase = encoder_layerdrop
lowercase = decoder_layerdrop
lowercase = encoder_layers
lowercase = auxiliary_loss
lowercase = position_embedding_type
lowercase = backbone
lowercase = use_pretrained_backbone
lowercase = dilation
# Hungarian matcher
lowercase = class_cost
lowercase = bbox_cost
lowercase = giou_cost
# Loss coefficients
lowercase = mask_loss_coefficient
lowercase = dice_loss_coefficient
lowercase = cls_loss_coefficient
lowercase = bbox_loss_coefficient
lowercase = giou_loss_coefficient
lowercase = focal_alpha
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.d_model
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 12
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import os
import numpy
import onnx
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = a.name
lowercase = b.name
lowercase = ''
lowercase = ''
lowercase = a == b
lowercase = name_a
lowercase = name_b
return res
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
_graph_replace_input_with(node_proto.attribute[1].g , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for n in graph_proto.node:
_node_replace_input_with(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = list(model.graph.initializer )
lowercase = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
lowercase = inits[i].name
lowercase = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = os.path.dirname(__SCREAMING_SNAKE_CASE )
lowercase = os.path.basename(__SCREAMING_SNAKE_CASE )
lowercase = onnx.load(os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
lowercase = list(model.graph.initializer )
lowercase = set()
lowercase = {}
lowercase = []
lowercase = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(__SCREAMING_SNAKE_CASE ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(__SCREAMING_SNAKE_CASE )
dup_set.add(__SCREAMING_SNAKE_CASE )
lowercase = inits[j].data_type
lowercase = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('unexpected data type: ' , __SCREAMING_SNAKE_CASE )
total_reduced_size += mem_size
lowercase = inits[i].name
lowercase = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(__SCREAMING_SNAKE_CASE )
else:
lowercase = [name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 1024 / 1024 / 1024 , 'GB' )
lowercase = sorted(__SCREAMING_SNAKE_CASE )
_remove_dup_initializers_from_model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = 'optimized_' + model_file_name
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
onnx.save(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return new_model
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if indegree[i] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
while queue:
lowercase = queue.pop(0 )
cnt += 1
topo.append(__SCREAMING_SNAKE_CASE )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
if cnt != len(__SCREAMING_SNAKE_CASE ):
print('Cycle exists' )
else:
print(__SCREAMING_SNAKE_CASE )
# Adjacency List of Graph
UpperCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 84 | 1 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=[1, 1, 2] , snake_case=1 , snake_case=32 , snake_case=4 , snake_case=8 , snake_case=37 , snake_case="gelu_new" , snake_case=0.1 , snake_case=0.1 , snake_case=0.0 , snake_case=512 , snake_case=3 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , snake_case=False , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = block_sizes
lowercase = num_decoder_layers
lowercase = d_model
lowercase = n_head
lowercase = d_head
lowercase = d_inner
lowercase = hidden_act
lowercase = hidden_dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = 2
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = initializer_std
# Used in the tests to check the size of the first attention layer
lowercase = n_head
# Used in the tests to check the size of the first hidden state
lowercase = self.d_model
# Used in the tests to check the number of output hidden states/attentions
lowercase = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
lowercase = self.num_hidden_layers + 2
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = TFFunnelModel(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
lowercase = [input_ids, input_mask]
lowercase = model(snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
lowercase = False
lowercase = TFFunnelModel(config=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
lowercase = False
lowercase = TFFunnelModel(config=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = TFFunnelBaseModel(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
lowercase = [input_ids, input_mask]
lowercase = model(snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
lowercase = False
lowercase = TFFunnelBaseModel(config=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
lowercase = False
lowercase = TFFunnelBaseModel(config=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = TFFunnelForPreTraining(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = TFFunnelForMaskedLM(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = self.num_labels
lowercase = TFFunnelForSequenceClassification(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = self.num_choices
lowercase = TFFunnelForMultipleChoice(config=snake_case )
lowercase = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
lowercase = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
lowercase = tf.tile(tf.expand_dims(snake_case , 1 ) , (1, self.num_choices, 1) )
lowercase = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
lowercase = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = self.num_labels
lowercase = TFFunnelForTokenClassification(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = TFFunnelForQuestionAnswering(config=snake_case )
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowercase = model(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 SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : str = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_UpperCamelCase : Tuple = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_UpperCamelCase : Optional[Any] = False
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFFunnelModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case )
@require_tf
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Any = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_UpperCamelCase : int = False
_UpperCamelCase : Dict = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFFunnelModelTester(self , base=snake_case )
lowercase = ConfigTester(self , config_class=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case )
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = tempfile.mkdtemp()
lowercase = 5
# Realm tok
lowercase = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'test',
'question',
'this',
'is',
'the',
'first',
'second',
'third',
'fourth',
'fifth',
'record',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowercase = os.path.join(self.tmpdirname , 'realm_tokenizer' )
os.makedirs(snake_case , exist_ok=snake_case )
lowercase = os.path.join(snake_case , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
lowercase = os.path.join(self.tmpdirname , 'realm_block_records' )
os.makedirs(snake_case , exist_ok=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'realm_tokenizer' ) )
def SCREAMING_SNAKE_CASE__ ( self ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = RealmConfig(num_block_records=self.num_block_records )
return config
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = Dataset.from_dict(
{
'id': ['0', '1'],
'question': ['foo', 'bar'],
'answers': [['Foo', 'Bar'], ['Bar']],
} )
return dataset
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = np.array(
[
B'This is the first record',
B'This is the second record',
B'This is the third record',
B'This is the fourth record',
B'This is the fifth record',
B'This is a longer longer longer record',
] , dtype=snake_case , )
return block_records
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_config()
lowercase = self.get_dummy_retriever()
lowercase = retriever.tokenizer
lowercase = np.array([0, 3] , dtype='long' )
lowercase = tokenizer(['Test question'] ).input_ids
lowercase = tokenizer(
['the fourth'] , add_special_tokens=snake_case , return_token_type_ids=snake_case , return_attention_mask=snake_case , ).input_ids
lowercase = config.reader_seq_len
lowercase , lowercase , lowercase , lowercase = retriever(
snake_case , snake_case , answer_ids=snake_case , max_length=snake_case , return_tensors='np' )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(len(snake_case ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]'] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]'] , )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_config()
lowercase = self.get_dummy_retriever()
lowercase = retriever.tokenizer
lowercase = np.array([0, 3, 5] , dtype='long' )
lowercase = tokenizer(['Test question'] ).input_ids
lowercase = tokenizer(
['the fourth', 'longer longer'] , add_special_tokens=snake_case , return_token_type_ids=snake_case , return_attention_mask=snake_case , ).input_ids
lowercase = config.reader_seq_len
lowercase , lowercase , lowercase , lowercase = retriever(
snake_case , snake_case , answer_ids=snake_case , max_length=snake_case , return_tensors='np' )
self.assertEqual([False, True, True] , snake_case )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , snake_case )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , 'realm_block_records' ) )
# Test local path
lowercase = retriever.from_pretrained(os.path.join(self.tmpdirname , 'realm_block_records' ) )
self.assertEqual(retriever.block_records[0] , B'This is the first record' )
# Test mocked remote path
with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
lowercase = os.path.join(
os.path.join(self.tmpdirname , 'realm_block_records' ) , _REALM_BLOCK_RECORDS_FILENAME )
lowercase = RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )
self.assertEqual(retriever.block_records[0] , B'This is the first record' )
| 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 1 |
import re
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
UpperCAmelCase = '''0094702343221'''
print(is_sri_lankan_phone_number(phone))
| 84 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=False , snake_case=True , snake_case=False , snake_case=True , snake_case=33 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = EsmModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
lowercase = model(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 SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = EsmForMaskedLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = self.num_labels
lowercase = EsmForTokenClassification(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : str = False
_UpperCamelCase : Dict = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCamelCase : Any = ()
_UpperCamelCase : Optional[Any] = (
{
"""feature-extraction""": EsmModel,
"""fill-mask""": EsmForMaskedLM,
"""text-classification""": EsmForSequenceClassification,
"""token-classification""": EsmForTokenClassification,
"""zero-shot""": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : Union[str, Any] = True
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = EsmModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = EsmModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()[0]
lowercase = EsmEmbeddings(config=snake_case )
lowercase = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
lowercase = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
lowercase = create_position_ids_from_input_ids(snake_case , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(snake_case , snake_case ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()[0]
lowercase = EsmEmbeddings(config=snake_case )
lowercase = torch.empty(2 , 4 , 30 )
lowercase = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
lowercase = torch.as_tensor([expected_single_positions, expected_single_positions] )
lowercase = embeddings.create_position_ids_from_inputs_embeds(snake_case )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(snake_case , snake_case ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@unittest.skip('Esm does not support embedding resizing' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_torch
class A_ ( __lowerCamelCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
with torch.no_grad():
lowercase = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowercase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowercase = model(snake_case )[0]
lowercase = 33
lowercase = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , snake_case )
lowercase = torch.tensor(
[[[8.9_215, -10.5_898, -6.4_671], [-6.3_967, -13.9_114, -1.1_212], [-7.7_812, -13.9_516, -3.7_406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
with torch.no_grad():
lowercase = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
lowercase = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase = model(snake_case )[0]
# compare the actual values for a slice.
lowercase = torch.tensor(
[[[0.1_444, 0.5_413, 0.3_248], [0.3_034, 0.0_053, 0.3_108], [0.3_228, -0.2_499, 0.3_415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , snake_case , atol=1E-4 ) )
| 84 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
UpperCAmelCase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ErnieForCausalLM''',
'''ErnieForMaskedLM''',
'''ErnieForMultipleChoice''',
'''ErnieForNextSentencePrediction''',
'''ErnieForPreTraining''',
'''ErnieForQuestionAnswering''',
'''ErnieForSequenceClassification''',
'''ErnieForTokenClassification''',
'''ErnieModel''',
'''ErniePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 | 1 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase = n_token
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [n_token]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowercase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowercase = nn.Parameter(torch.zeros(self.n_clusters ) )
lowercase = nn.ModuleList()
lowercase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
else:
self.out_projs.append(snake_case )
self.out_layers.append(nn.Linear(snake_case , snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
self.out_layers.append(nn.Linear(snake_case , r_idx - l_idx ) )
lowercase = keep_order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if proj is None:
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowercase = nn.functional.linear(snake_case , proj.t().contiguous() )
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None , snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
lowercase = hidden[..., :-1, :].contiguous()
lowercase = labels[..., 1:].contiguous()
lowercase = hidden.view(-1 , hidden.size(-1 ) )
lowercase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
lowercase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowercase = labels != -100
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = (
-nn.functional.log_softmax(snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowercase = nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
if labels is None:
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = 0
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowercase = (labels >= l_idx) & (labels < r_idx)
lowercase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowercase = labels.index_select(0 , snake_case ) - l_idx
lowercase = head_logprob.index_select(0 , snake_case )
lowercase = hidden.index_select(0 , snake_case )
else:
lowercase = hidden
if i == 0:
if labels is not None:
lowercase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowercase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowercase = logprob_i
if labels is not None:
if (hasattr(self , 'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 , snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = head_logprob[:, -i] + tail_logprob_i
lowercase = logprob_i
return out
| 84 |
import unittest
from transformers import 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 (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTDoubleHeadsModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = self.num_labels
lowercase = OpenAIGPTForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCamelCase : str = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=False ):
lowercase = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=snake_case , )
lowercase = inputs_dict['labels']
lowercase = inputs_dict['labels']
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=snake_case , )
lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = OpenAIGPTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(snake_case )
lowercase = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=snake_case ) # the president is
lowercase = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 84 | 1 |
from collections.abc import Callable
import numpy as np
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = int(np.ceil((x_end - xa) / step_size ) )
lowercase = np.zeros((n + 1,) )
lowercase = ya
lowercase = xa
for k in range(__SCREAMING_SNAKE_CASE ):
lowercase = y[k] + step_size * ode_func(__SCREAMING_SNAKE_CASE , y[k] )
lowercase = y[k] + (
(step_size / 2) * (ode_func(__SCREAMING_SNAKE_CASE , y[k] ) + ode_func(x + step_size , __SCREAMING_SNAKE_CASE ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
lowercase = False
lowercase = index + i
lowercase = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 9999_6666_3333 ):
lowercase = math.floor(math.sqrt(__SCREAMING_SNAKE_CASE ) ) + 100
lowercase = prime_sieve(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = primes[prime_index]
while (last_prime**2) <= limit:
lowercase = primes[prime_index + 1]
lowercase = last_prime**2
lowercase = next_prime**2
# Get numbers divisible by lps(current)
lowercase = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowercase = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowercase = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowercase = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 84 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
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
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = """new-model"""
if is_tf_available():
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = NewModelConfig
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'bert-base-cased'
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'bert-base-cased'
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForPreTraining.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForCausalLM.from_pretrained(snake_case )
lowercase , lowercase = TFAutoModelForCausalLM.from_pretrained(snake_case , output_loading_info=snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelWithLMHead.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForMaskedLM.from_pretrained(snake_case )
lowercase , lowercase = TFAutoModelForMaskedLM.from_pretrained(snake_case , output_loading_info=snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(snake_case )
lowercase , lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(snake_case , output_loading_info=snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForSequenceClassification.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForQuestionAnswering.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
@slow
@require_tensorflow_probability
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
lowercase = AutoConfig.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
lowercase = TFAutoModelForTableQuestionAnswering.from_pretrained(snake_case )
lowercase , lowercase = TFAutoModelForTableQuestionAnswering.from_pretrained(
snake_case , output_loading_info=snake_case )
self.assertIsNotNone(snake_case )
self.assertIsInstance(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFAutoModelWithLMHead.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=snake_case ) , 1_4410 )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFAutoModelWithLMHead.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=snake_case ) , 1_4410 )
def SCREAMING_SNAKE_CASE__ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
lowercase = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(snake_case , snake_case )
lowercase = copy.deepcopy(model.config )
lowercase = ['FunnelBaseModel']
lowercase = TFAutoModel.from_config(snake_case )
self.assertIsInstance(snake_case , snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(snake_case )
lowercase = TFAutoModel.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
try:
AutoConfig.register('new-model' , snake_case )
lowercase = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(snake_case ):
auto_class.register(snake_case , snake_case )
auto_class.register(snake_case , snake_case )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case ):
auto_class.register(snake_case , snake_case )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase = BertModelTester(self ).get_config()
lowercase = NewModelConfig(**tiny_config.to_dict() )
lowercase = auto_class.from_config(snake_case )
self.assertIsInstance(snake_case , snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(snake_case )
lowercase = auto_class.from_pretrained(snake_case )
self.assertIsInstance(snake_case , snake_case )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def SCREAMING_SNAKE_CASE__ ( self ):
with self.assertRaisesRegex(
snake_case , 'bert-base is not a local folder and is not a valid model identifier' ):
lowercase = TFAutoModel.from_pretrained('bert-base' )
def SCREAMING_SNAKE_CASE__ ( self ):
with self.assertRaisesRegex(
snake_case , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
lowercase = TFAutoModel.from_pretrained(snake_case , revision='aaaaaa' )
def SCREAMING_SNAKE_CASE__ ( self ):
with self.assertRaisesRegex(
snake_case , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
lowercase = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def SCREAMING_SNAKE_CASE__ ( self ):
with self.assertRaisesRegex(snake_case , 'Use `from_pt=True` to load this model' ):
lowercase = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def SCREAMING_SNAKE_CASE__ ( self ):
# Make sure we have cached the model.
lowercase = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
lowercase = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
lowercase = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
lowercase = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 84 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
UpperCAmelCase = re.compile(R'''^\s*try:''')
# Catches a line with else:
UpperCAmelCase = re.compile(R'''^\s*else:''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if _re_test_backend.search(__SCREAMING_SNAKE_CASE ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ):
lowercase = _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ).groups()[0]
lowercase = re.findall(r'\[([^\]]+)\]' , __SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_between_brackets.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
def find_duplicates(__SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def UpperCAmelCase_ ( ):
lowercase = []
for root, _, files in os.walk(__SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' )
lowercase = parse_init(__SCREAMING_SNAKE_CASE )
if objects is not None:
lowercase = analyze_results(*__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = []
for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / folder).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace(os.path.sep , '.' )
submodules.append(__SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / fname).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__SCREAMING_SNAKE_CASE )
return submodules
UpperCAmelCase = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def UpperCAmelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
lowercase = direct_transformers_import(__SCREAMING_SNAKE_CASE )
lowercase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
lowercase = f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , __SCREAMING_SNAKE_CASE ) ) )
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 84 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = FileLock(str(tmpdir / 'foo.lock' ) )
lowercase = FileLock(str(tmpdir / 'foo.lock' ) )
lowercase = 0.01
with locka.acquire():
with pytest.raises(__SCREAMING_SNAKE_CASE ):
lowercase = time.time()
locka.acquire(__SCREAMING_SNAKE_CASE )
assert time.time() - _start > timeout
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = 'a' * 1000 + '.lock'
lowercase = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith('.lock' )
assert not locka._lock_file.endswith(__SCREAMING_SNAKE_CASE )
assert len(os.path.basename(locka._lock_file ) ) <= 255
lowercase = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(__SCREAMING_SNAKE_CASE ):
locka.acquire(0 )
| 84 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = None
def __str__( self ):
return F'''{self.data}'''
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
lowercase = None
def __iter__( self ):
lowercase = self.top
while node:
yield node.data
lowercase = node.next
def __str__( self ):
return "->".join([str(snake_case ) for item in self] )
def __len__( self ):
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.top is None
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = Node(snake_case )
if not self.is_empty():
lowercase = self.top
lowercase = node
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case )
lowercase = self.top
lowercase = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 | 1 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = 0
lowercase = 0
while num > 0:
lowercase = num % 8
lowercase = octal + (remainder * math.floor(math.pow(10 , __SCREAMING_SNAKE_CASE ) ))
counter += 1
lowercase = math.floor(num / 8 ) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return F'''0o{int(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( ):
print('\n2 in octal is:' )
print(decimal_to_octal(2 ) ) # = 2
print('\n8 in octal is:' )
print(decimal_to_octal(8 ) ) # = 10
print('\n65 in octal is:' )
print(decimal_to_octal(65 ) ) # = 101
print('\n216 in octal is:' )
print(decimal_to_octal(216 ) ) # = 330
print('\n512 in octal is:' )
print(decimal_to_octal(512 ) ) # = 1000
print('\n' )
if __name__ == "__main__":
main()
| 84 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 | 1 |
import requests
UpperCAmelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# fetching a list of articles in json format
lowercase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
| 84 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : Dict = """dummy_data"""
_UpperCamelCase : Optional[int] = """datasets"""
_UpperCamelCase : Tuple = False
def __init__( self , snake_case , snake_case , snake_case , snake_case = None , snake_case = False , snake_case = True , snake_case = None , ):
lowercase = 0
lowercase = dataset_name
lowercase = cache_dir
lowercase = use_local_dummy_data
lowercase = config
# download_callbacks take a single url as input
lowercase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase = str(snake_case )
# to be downloaded
lowercase = None
lowercase = None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._dummy_file is None:
lowercase = self.download_dummy_data()
return self._dummy_file
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase = cached_path(
snake_case , cache_dir=self.cache_dir , extract_compressed_file=snake_case , force_extract=snake_case )
return os.path.join(snake_case , self.dummy_file_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._bucket_url is None:
lowercase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def SCREAMING_SNAKE_CASE__ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case , snake_case ):
return self.create_dummy_data_dict(snake_case , snake_case )
elif isinstance(snake_case , (list, tuple) ):
return self.create_dummy_data_list(snake_case , snake_case )
else:
return self.create_dummy_data_single(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case , **snake_case ):
return path
def SCREAMING_SNAKE_CASE__ ( self ):
return {}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case , snake_case ):
for single_url in single_urls:
download_callback(snake_case )
else:
lowercase = single_urls
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(snake_case , snake_case ):
lowercase = [os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) ) for x in single_urls]
else:
lowercase = single_urls
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) )
lowercase = value
# make sure that values are unique
if all(isinstance(snake_case , snake_case ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , snake_case ) ) for url in data_url )
lowercase = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase = [data_url[0]] * len(snake_case )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(snake_case )
return dummy_data_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(snake_case ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
def _iter_archive_members(snake_case ):
# this preserves the order of the members inside the ZIP archive
lowercase = Path(self.dummy_file ).parent
lowercase = path.relative_to(snake_case )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case )
lowercase = Path(snake_case )
lowercase = _iter_archive_members(snake_case ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(snake_case ).as_posix(), file_path.open('rb' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
lowercase = [paths]
for path in paths:
if os.path.isfile(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(snake_case ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(snake_case , snake_case )
| 84 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
UpperCAmelCase = '''\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
'''
UpperCAmelCase = '''\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
'''
UpperCAmelCase = '''
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: "c" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric(\'mauve\')
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/krishnap25/mauve' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/krishnap25/mauve'] , reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case=None , snake_case="auto" , snake_case=-1 , snake_case=0.9 , snake_case=5 , snake_case=500 , snake_case="gpt2-large" , snake_case=-1 , snake_case=1024 , snake_case=25 , snake_case=5 , snake_case=True , snake_case=25 , ):
lowercase = compute_mauve(
p_text=snake_case , q_text=snake_case , p_features=snake_case , q_features=snake_case , p_tokens=snake_case , q_tokens=snake_case , num_buckets=snake_case , pca_max_data=snake_case , kmeans_explained_var=snake_case , kmeans_num_redo=snake_case , kmeans_max_iter=snake_case , featurize_model_name=snake_case , device_id=snake_case , max_text_length=snake_case , divergence_curve_discretization_size=snake_case , mauve_scaling_factor=snake_case , verbose=snake_case , seed=snake_case , )
return out
| 84 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('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 SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84 | 1 |
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
UpperCAmelCase = logging.get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : str
_UpperCamelCase : str = None
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , **snake_case ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( cls ):
return F'''`pip install {cls.pip_package or cls.name}`'''
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = """optuna"""
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
return is_optuna_available()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , **snake_case ):
return run_hp_search_optuna(snake_case , snake_case , snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return default_hp_space_optuna(snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = """ray"""
_UpperCamelCase : Optional[Any] = """'ray[tune]'"""
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
return is_ray_available()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , **snake_case ):
return run_hp_search_ray(snake_case , snake_case , snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return default_hp_space_ray(snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = """sigopt"""
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
return is_sigopt_available()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , **snake_case ):
return run_hp_search_sigopt(snake_case , snake_case , snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return default_hp_space_sigopt(snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = """wandb"""
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
return is_wandb_available()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , **snake_case ):
return run_hp_search_wandb(snake_case , snake_case , snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return default_hp_space_wandb(snake_case )
UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ):
lowercase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = available_backends[0].name
if len(__SCREAMING_SNAKE_CASE ) > 1:
logger.info(
F'''{len(__SCREAMING_SNAKE_CASE )} 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() ) )
| 84 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_terminal_summary_main
lowercase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE , id=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase = 0
# Doctest custom flag to ignore output.
UpperCAmelCase = doctest.register_optionflag('''IGNORE_RESULT''')
UpperCAmelCase = doctest.OutputChecker
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , snake_case , snake_case , snake_case )
UpperCAmelCase = CustomOutputChecker
UpperCAmelCase = HfDoctestModule
UpperCAmelCase = HfDocTestParser
| 84 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Dict = DDIMPipeline
_UpperCamelCase : int = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_UpperCamelCase : Any = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""latents""",
"""callback""",
"""callback_steps""",
}
_UpperCamelCase : List[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
_UpperCamelCase : Any = False
def SCREAMING_SNAKE_CASE__ ( self ):
torch.manual_seed(0 )
lowercase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
lowercase = DDIMScheduler()
lowercase = {'unet': unet, 'scheduler': scheduler}
return components
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=0 ):
if str(snake_case ).startswith('mps' ):
lowercase = torch.manual_seed(snake_case )
else:
lowercase = torch.Generator(device=snake_case ).manual_seed(snake_case )
lowercase = {
'batch_size': 1,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'cpu'
lowercase = self.get_dummy_components()
lowercase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_dummy_inputs(snake_case )
lowercase = pipe(**snake_case ).images
lowercase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
lowercase = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowercase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def SCREAMING_SNAKE_CASE__ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE__ ( self ):
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE__ ( self ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE__ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'google/ddpm-cifar10-32'
lowercase = UNetaDModel.from_pretrained(snake_case )
lowercase = DDIMScheduler()
lowercase = DDIMPipeline(unet=snake_case , scheduler=snake_case )
ddim.to(snake_case )
ddim.set_progress_bar_config(disable=snake_case )
lowercase = torch.manual_seed(0 )
lowercase = ddim(generator=snake_case , eta=0.0 , output_type='numpy' ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase = np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'google/ddpm-ema-bedroom-256'
lowercase = UNetaDModel.from_pretrained(snake_case )
lowercase = DDIMScheduler.from_pretrained(snake_case )
lowercase = DDIMPipeline(unet=snake_case , scheduler=snake_case )
ddpm.to(snake_case )
ddpm.set_progress_bar_config(disable=snake_case )
lowercase = torch.manual_seed(0 )
lowercase = ddpm(generator=snake_case , output_type='numpy' ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase = np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 84 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase = n_token
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [n_token]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowercase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowercase = nn.Parameter(torch.zeros(self.n_clusters ) )
lowercase = nn.ModuleList()
lowercase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
else:
self.out_projs.append(snake_case )
self.out_layers.append(nn.Linear(snake_case , snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
self.out_layers.append(nn.Linear(snake_case , r_idx - l_idx ) )
lowercase = keep_order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if proj is None:
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowercase = nn.functional.linear(snake_case , proj.t().contiguous() )
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None , snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
lowercase = hidden[..., :-1, :].contiguous()
lowercase = labels[..., 1:].contiguous()
lowercase = hidden.view(-1 , hidden.size(-1 ) )
lowercase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
lowercase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowercase = labels != -100
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = (
-nn.functional.log_softmax(snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowercase = nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
if labels is None:
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = 0
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowercase = (labels >= l_idx) & (labels < r_idx)
lowercase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowercase = labels.index_select(0 , snake_case ) - l_idx
lowercase = head_logprob.index_select(0 , snake_case )
lowercase = hidden.index_select(0 , snake_case )
else:
lowercase = hidden
if i == 0:
if labels is not None:
lowercase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowercase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowercase = logprob_i
if labels is not None:
if (hasattr(self , 'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 , snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = head_logprob[:, -i] + tail_logprob_i
lowercase = logprob_i
return out
| 84 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
UpperCAmelCase = logging.get_logger(__name__)
# General docstring
UpperCAmelCase = '''RegNetConfig'''
# Base docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = [1, 1088, 7, 7]
# Image classification docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = '''tabby, tabby cat'''
UpperCAmelCase = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = 3 , snake_case = 1 , snake_case = 1 , snake_case = "relu" , **snake_case , ):
super().__init__(**snake_case )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
lowercase = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
lowercase = tf.keras.layers.ConvaD(
filters=snake_case , kernel_size=snake_case , strides=snake_case , padding='VALID' , groups=snake_case , use_bias=snake_case , name='convolution' , )
lowercase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
lowercase = ACTaFN[activation] if activation is not None else tf.identity
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.convolution(self.padding(snake_case ) )
lowercase = self.normalization(snake_case )
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , **snake_case ):
super().__init__(**snake_case )
lowercase = config.num_channels
lowercase = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name='embedder' , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = shape_list(snake_case )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
lowercase = tf.transpose(snake_case , perm=(0, 2, 3, 1) )
lowercase = self.embedder(snake_case )
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = 2 , **snake_case ):
super().__init__(**snake_case )
lowercase = tf.keras.layers.ConvaD(
filters=snake_case , kernel_size=1 , strides=snake_case , use_bias=snake_case , name='convolution' )
lowercase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name='normalization' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = False ):
return self.normalization(self.convolution(snake_case ) , training=snake_case )
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , **snake_case ):
super().__init__(**snake_case )
lowercase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case , name='pooler' )
lowercase = [
tf.keras.layers.ConvaD(filters=snake_case , kernel_size=1 , activation='relu' , name='attention.0' ),
tf.keras.layers.ConvaD(filters=snake_case , kernel_size=1 , activation='sigmoid' , name='attention.2' ),
]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
lowercase = self.pooler(snake_case )
for layer_module in self.attention:
lowercase = layer_module(snake_case )
lowercase = hidden_state * pooled
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 1 , **snake_case ):
super().__init__(**snake_case )
lowercase = in_channels != out_channels or stride != 1
lowercase = max(1 , out_channels // config.groups_width )
lowercase = (
TFRegNetShortCut(snake_case , stride=snake_case , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
lowercase = [
TFRegNetConvLayer(snake_case , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
snake_case , stride=snake_case , groups=snake_case , activation=config.hidden_act , name='layer.1' ),
TFRegNetConvLayer(snake_case , kernel_size=1 , activation=snake_case , name='layer.2' ),
]
lowercase = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = hidden_state
for layer_module in self.layers:
lowercase = layer_module(snake_case )
lowercase = self.shortcut(snake_case )
hidden_state += residual
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 1 , **snake_case ):
super().__init__(**snake_case )
lowercase = in_channels != out_channels or stride != 1
lowercase = max(1 , out_channels // config.groups_width )
lowercase = (
TFRegNetShortCut(snake_case , stride=snake_case , name='shortcut' )
if should_apply_shortcut
else tf.keras.layers.Activation('linear' , name='shortcut' )
)
lowercase = [
TFRegNetConvLayer(snake_case , kernel_size=1 , activation=config.hidden_act , name='layer.0' ),
TFRegNetConvLayer(
snake_case , stride=snake_case , groups=snake_case , activation=config.hidden_act , name='layer.1' ),
TFRegNetSELayer(snake_case , reduced_channels=int(round(in_channels / 4 ) ) , name='layer.2' ),
TFRegNetConvLayer(snake_case , kernel_size=1 , activation=snake_case , name='layer.3' ),
]
lowercase = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = hidden_state
for layer_module in self.layers:
lowercase = layer_module(snake_case )
lowercase = self.shortcut(snake_case )
hidden_state += residual
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 2 , snake_case = 2 , **snake_case ):
super().__init__(**snake_case )
lowercase = TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
lowercase = [
# downsampling is done in the first layer with stride of 2
layer(snake_case , snake_case , snake_case , stride=snake_case , name='layers.0' ),
*[layer(snake_case , snake_case , snake_case , name=F'''layers.{i+1}''' ) for i in range(depth - 1 )],
]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
for layer_module in self.layers:
lowercase = layer_module(snake_case )
return hidden_state
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , snake_case , **snake_case ):
super().__init__(**snake_case )
lowercase = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
snake_case , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name='stages.0' , ) )
lowercase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(snake_case , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(snake_case , snake_case , snake_case , depth=snake_case , name=F'''stages.{i+1}''' ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = False , snake_case = True ):
lowercase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase = hidden_states + (hidden_state,)
lowercase = stage_module(snake_case )
if output_hidden_states:
lowercase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case , hidden_states=snake_case )
@keras_serializable
class A_ ( tf.keras.layers.Layer ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = RegNetConfig
def __init__( self , snake_case , **snake_case ):
super().__init__(**snake_case )
lowercase = config
lowercase = TFRegNetEmbeddings(snake_case , name='embedder' )
lowercase = TFRegNetEncoder(snake_case , name='encoder' )
lowercase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case , name='pooler' )
@unpack_inputs
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = None , snake_case = False , ):
lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.embedder(snake_case , training=snake_case )
lowercase = self.encoder(
snake_case , output_hidden_states=snake_case , return_dict=snake_case , training=snake_case )
lowercase = encoder_outputs[0]
lowercase = self.pooler(snake_case )
# Change to NCHW output format have uniformity in the modules
lowercase = tf.transpose(snake_case , perm=(0, 3, 1, 2) )
lowercase = tf.transpose(snake_case , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
lowercase = tuple([tf.transpose(snake_case , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case , pooler_output=snake_case , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = RegNetConfig
_UpperCamelCase : List[Any] = """regnet"""
_UpperCamelCase : Optional[int] = """pixel_values"""
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) , dtype=tf.floataa )}
UpperCAmelCase = R'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
UpperCAmelCase = R'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , __lowerCamelCase , )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , *snake_case , **snake_case ):
super().__init__(snake_case , *snake_case , **snake_case )
lowercase = TFRegNetMainLayer(snake_case , name='regnet' )
@unpack_inputs
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = None , snake_case=False , ):
lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.regnet(
pixel_values=snake_case , output_hidden_states=snake_case , return_dict=snake_case , training=snake_case , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , __lowerCamelCase , )
class A_ ( __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , *snake_case , **snake_case ):
super().__init__(snake_case , *snake_case , **snake_case )
lowercase = config.num_labels
lowercase = TFRegNetMainLayer(snake_case , name='regnet' )
# classification head
lowercase = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name='classifier.1' ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case=False , ):
lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.regnet(
snake_case , output_hidden_states=snake_case , return_dict=snake_case , training=snake_case )
lowercase = outputs.pooler_output if return_dict else outputs[1]
lowercase = self.classifier[0](snake_case )
lowercase = self.classifier[1](snake_case )
lowercase = None if labels is None else self.hf_compute_loss(labels=snake_case , logits=snake_case )
if not return_dict:
lowercase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=snake_case , logits=snake_case , hidden_states=outputs.hidden_states )
| 84 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.' )
if len(snake_case ) != 0:
lowercase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(snake_case ) != cols:
raise error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise error
lowercase = rows
else:
lowercase = []
def SCREAMING_SNAKE_CASE__ ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows[0] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return (self.num_rows, self.num_columns)
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.order[0] == self.order[1]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return bool(self.determinant() )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(snake_case ).determinant()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if (row + column) % 2 == 0:
return self.get_minor(snake_case , snake_case )
return -1 * self.get_minor(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[self.get_minor(snake_case , snake_case ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
for row in self.rows
] )
+ "]"
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError('Row must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix' )
if position is None:
self.rows.append(snake_case )
else:
lowercase = self.rows[0:position] + [row] + self.rows[position:]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError(
'Column must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in column:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix' )
if position is None:
lowercase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , snake_case ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , snake_case ):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , snake_case ):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , snake_case ):
if isinstance(snake_case , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(snake_case , snake_case ):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second' )
return Matrix(
[
[Matrix.dot_product(snake_case , snake_case ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix' )
def __pow__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise TypeError('A Matrix can only be raised to the power of an int' )
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power' )
lowercase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case ):
return sum(row[i] * column[i] for i in range(len(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 1 |
from math import isqrt, loga
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = False
return [i for i in range(2 , __SCREAMING_SNAKE_CASE ) if is_prime[i]]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 80_0800 , __SCREAMING_SNAKE_CASE = 80_0800 ):
lowercase = degree * loga(__SCREAMING_SNAKE_CASE )
lowercase = int(__SCREAMING_SNAKE_CASE )
lowercase = calculate_prime_numbers(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = len(__SCREAMING_SNAKE_CASE ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 84 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=8 ):
lowercase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , ):
super().__init__()
self.register_modules(
unet=snake_case , scheduler=snake_case , movq=snake_case , )
lowercase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
if latents is None:
lowercase = randn_tensor(snake_case , generator=snake_case , device=snake_case , dtype=snake_case )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase = latents.to(snake_case )
lowercase = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
lowercase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=snake_case )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase , lowercase = cpu_offload_with_hook(snake_case , snake_case , prev_module_hook=snake_case )
# We'll offload the last model manually.
lowercase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE__ ( self ):
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(snake_case , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(snake_case )
def __call__( self , snake_case , snake_case , snake_case = 512 , snake_case = 512 , snake_case = 100 , snake_case = 4.0 , snake_case = 1 , snake_case = None , snake_case = None , snake_case = "pil" , snake_case = True , ):
lowercase = self._execution_device
lowercase = guidance_scale > 1.0
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
lowercase = image_embeds.shape[0] * num_images_per_prompt
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
if do_classifier_free_guidance:
lowercase = image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = negative_image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=snake_case )
self.scheduler.set_timesteps(snake_case , device=snake_case )
lowercase = self.scheduler.timesteps
lowercase = self.unet.config.in_channels
lowercase , lowercase = downscale_height_and_width(snake_case , snake_case , self.movq_scale_factor )
# create initial latent
lowercase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , snake_case , snake_case , snake_case , self.scheduler , )
for i, t in enumerate(self.progress_bar(snake_case ) ):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase = {'image_embeds': image_embeds}
lowercase = self.unet(
sample=snake_case , timestep=snake_case , encoder_hidden_states=snake_case , added_cond_kwargs=snake_case , return_dict=snake_case , )[0]
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
lowercase , lowercase = noise_pred.chunk(2 )
lowercase , lowercase = variance_pred.chunk(2 )
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(
snake_case , snake_case , snake_case , generator=snake_case , )[0]
# post-processing
lowercase = self.movq.decode(snake_case , force_not_quantize=snake_case )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase = image * 0.5 + 0.5
lowercase = image.clamp(0 , 1 )
lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case )
| 84 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Node | None
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = None
for i in sorted(snake_case , reverse=snake_case ):
lowercase = Node(snake_case , self.head )
def __iter__( self ):
lowercase = self.head
while node:
yield node.data
lowercase = node.next_node
def __len__( self ):
return sum(1 for _ in self )
def __str__( self ):
return " -> ".join([str(snake_case ) for node in self] )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return SortedLinkedList(list(__SCREAMING_SNAKE_CASE ) + list(__SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 | 1 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( ):
# Get the sagemaker specific mp parameters from smp_options variable.
lowercase = os.getenv('SM_HP_MP_PARAMETERS' , '{}' )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
lowercase = json.loads(__SCREAMING_SNAKE_CASE )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
lowercase = os.getenv('SM_FRAMEWORK_PARAMS' , '{}' )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
lowercase = json.loads(__SCREAMING_SNAKE_CASE )
if not mpi_options.get('sagemaker_mpi_enabled' , __SCREAMING_SNAKE_CASE ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec('smdistributed' ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : str = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def SCREAMING_SNAKE_CASE__ ( self ):
super().__post_init__()
warnings.warn(
'`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
'`TrainingArguments` instead.' , snake_case , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
logger.info('PyTorch: setting up devices' )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
'torch.distributed process group is initialized, but local_rank == -1. '
'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch' )
if self.no_cuda:
lowercase = torch.device('cpu' )
lowercase = 0
elif is_sagemaker_model_parallel_available():
lowercase = smp.local_rank()
lowercase = torch.device('cuda' , snake_case )
lowercase = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend='smddp' , timeout=self.ddp_timeout_delta )
lowercase = int(os.getenv('SMDATAPARALLEL_LOCAL_RANK' ) )
lowercase = torch.device('cuda' , self.local_rank )
lowercase = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
lowercase = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
lowercase = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='nccl' , timeout=self.ddp_timeout_delta )
lowercase = torch.device('cuda' , self.local_rank )
lowercase = 1
if device.type == "cuda":
torch.cuda.set_device(snake_case )
return device
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return not is_sagemaker_model_parallel_available()
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return False
| 84 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCREAMING_SNAKE_CASE ):
if is_palindrome(__SCREAMING_SNAKE_CASE ) and is_palindrome(bin(__SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 84 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.''' )
if tokenizer_name is None:
lowercase = TOKENIZER_CLASSES
else:
lowercase = {tokenizer_name: getattr(__SCREAMING_SNAKE_CASE , tokenizer_name + 'Fast' )}
logger.info(F'''Loading tokenizer classes: {tokenizer_names}''' )
for tokenizer_name in tokenizer_names:
lowercase = TOKENIZER_CLASSES[tokenizer_name]
lowercase = True
if checkpoint_name is None:
lowercase = list(tokenizer_class.max_model_input_sizes.keys() )
else:
lowercase = [checkpoint_name]
logger.info(F'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}''' )
for checkpoint in checkpoint_names:
logger.info(F'''Loading {tokenizer_class.__class__.__name__} {checkpoint}''' )
# Load tokenizer
lowercase = tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , force_download=__SCREAMING_SNAKE_CASE )
# Save fast tokenizer
logger.info(F'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}''' )
# For organization names we create sub-directories
if "/" in checkpoint:
lowercase , lowercase = checkpoint.split('/' )
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif add_prefix:
lowercase = checkpoint
lowercase = dump_path
else:
lowercase = None
lowercase = dump_path
logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
lowercase = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
lowercase = file_path.split(__SCREAMING_SNAKE_CASE )[-1][0]
if next_char == "/":
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = None
logger.info(F'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
lowercase = tokenizer.save_pretrained(
__SCREAMING_SNAKE_CASE , legacy_format=__SCREAMING_SNAKE_CASE , filename_prefix=__SCREAMING_SNAKE_CASE )
logger.info(F'''=> File names {file_names}''' )
for file_name in file_names:
if not file_name.endswith('tokenizer.json' ):
os.remove(__SCREAMING_SNAKE_CASE )
logger.info(F'''=> removing {file_name}''' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
)
parser.add_argument(
'''--tokenizer_name''',
default=None,
type=str,
help=(
F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
'''download and convert all the checkpoints from AWS.'''
),
)
parser.add_argument(
'''--checkpoint_name''',
default=None,
type=str,
help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
)
parser.add_argument(
'''--force_download''',
action='''store_true''',
help='''Re-download checkpoints.''',
)
UpperCAmelCase = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 84 |
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """conditional_detr"""
_UpperCamelCase : Any = ["""past_key_values"""]
_UpperCamelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , snake_case=True , snake_case=None , snake_case=3 , snake_case=300 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case="relu" , snake_case=256 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1.0 , snake_case=False , snake_case="sine" , snake_case="resnet50" , snake_case=True , snake_case=False , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=1 , snake_case=1 , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=0.25 , **snake_case , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case , snake_case ):
lowercase = backbone_config.get('model_type' )
lowercase = CONFIG_MAPPING[backbone_model_type]
lowercase = config_class.from_dict(snake_case )
lowercase = use_timm_backbone
lowercase = backbone_config
lowercase = num_channels
lowercase = num_queries
lowercase = d_model
lowercase = encoder_ffn_dim
lowercase = encoder_layers
lowercase = encoder_attention_heads
lowercase = decoder_ffn_dim
lowercase = decoder_layers
lowercase = decoder_attention_heads
lowercase = dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = activation_function
lowercase = init_std
lowercase = init_xavier_std
lowercase = encoder_layerdrop
lowercase = decoder_layerdrop
lowercase = encoder_layers
lowercase = auxiliary_loss
lowercase = position_embedding_type
lowercase = backbone
lowercase = use_pretrained_backbone
lowercase = dilation
# Hungarian matcher
lowercase = class_cost
lowercase = bbox_cost
lowercase = giou_cost
# Loss coefficients
lowercase = mask_loss_coefficient
lowercase = dice_loss_coefficient
lowercase = cls_loss_coefficient
lowercase = bbox_loss_coefficient
lowercase = giou_loss_coefficient
lowercase = focal_alpha
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.d_model
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 12
| 84 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
UpperCAmelCase = sys.version_info >= (3, 10)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ):
return field(default_factory=lambda: default , metadata=__SCREAMING_SNAKE_CASE )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : float
_UpperCamelCase : str
_UpperCamelCase : bool
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int = 42
_UpperCamelCase : str = field(default="""toto""" , metadata={"""help""": """help message"""} )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : bool = False
_UpperCamelCase : bool = True
_UpperCamelCase : Optional[bool] = None
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = """titi"""
_UpperCamelCase : List[Any] = """toto"""
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = """titi"""
_UpperCamelCase : int = """toto"""
_UpperCamelCase : str = 42
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : BasicEnum = "toto"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = BasicEnum(self.foo )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : MixedTypeEnum = "toto"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = MixedTypeEnum(self.foo )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : Optional[int] = None
_UpperCamelCase : Optional[float] = field(default=__lowerCamelCase , metadata={"""help""": """help message"""} )
_UpperCamelCase : Optional[str] = None
_UpperCamelCase : Optional[List[str]] = list_field(default=[] )
_UpperCamelCase : Optional[List[int]] = list_field(default=[] )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : List[int] = list_field(default=[] )
_UpperCamelCase : List[int] = list_field(default=[1, 2, 3] )
_UpperCamelCase : List[str] = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
_UpperCamelCase : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : List[int] = field()
_UpperCamelCase : str = field()
_UpperCamelCase : BasicEnum = field()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = BasicEnum(self.required_enum )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : "BasicEnum" = field()
_UpperCamelCase : "Optional[bool]" = None
_UpperCamelCase : "str" = field(default="""toto""" , metadata={"""help""": """help message"""} )
_UpperCamelCase : "List[str]" = list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
if is_python_no_less_than_3_10:
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : bool = False
_UpperCamelCase : bool = True
_UpperCamelCase : bool | None = None
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : int | None = None
_UpperCamelCase : float | None = field(default=__lowerCamelCase , metadata={"""help""": """help message"""} )
_UpperCamelCase : str | None = None
_UpperCamelCase : list[str] | None = list_field(default=[] )
_UpperCamelCase : list[int] | None = list_field(default=[] )
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
lowercase = {k: v for k, v in vars(snake_case ).items() if k != 'container'}
lowercase = {k: v for k, v in vars(snake_case ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , snake_case ) and yy.get('choices' , snake_case ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](snake_case ) , yy['type'](snake_case ) )
del xx["type"], yy["type"]
self.assertEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , required=snake_case )
expected.add_argument('--bar' , type=snake_case , required=snake_case )
expected.add_argument('--baz' , type=snake_case , required=snake_case )
expected.add_argument('--flag' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
self.argparsersEqual(snake_case , snake_case )
lowercase = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((lowercase) , ) = parser.parse_args_into_dataclasses(snake_case , look_for_args_file=snake_case )
self.assertFalse(example.flag )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=snake_case )
expected.add_argument('--baz' , default='toto' , type=snake_case , help='help message' )
self.argparsersEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
expected.add_argument('--baz' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=snake_case , dest='baz' )
expected.add_argument('--opt' , type=snake_case , default=snake_case )
lowercase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(snake_case )
for dataclass_type in dataclass_types:
lowercase = HfArgumentParser(snake_case )
self.argparsersEqual(snake_case , snake_case )
lowercase = parser.parse_args([] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
lowercase = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
lowercase = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
lowercase = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
lowercase = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(snake_case , snake_case )
lowercase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
lowercase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
lowercase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
lowercase = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
lowercase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
lowercase = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def SCREAMING_SNAKE_CASE__ ( self ):
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : Literal["titi", "toto", 42] = "toto"
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(snake_case , snake_case )
lowercase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
lowercase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
lowercase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=snake_case )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=snake_case )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=snake_case )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=snake_case )
self.argparsersEqual(snake_case , snake_case )
lowercase = parser.parse_args([] )
self.assertEqual(
snake_case , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
lowercase = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(snake_case , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=snake_case , type=snake_case )
expected.add_argument('--bar' , default=snake_case , type=snake_case , help='help message' )
expected.add_argument('--baz' , default=snake_case , type=snake_case )
expected.add_argument('--ces' , nargs='+' , default=[] , type=snake_case )
expected.add_argument('--des' , nargs='+' , default=[] , type=snake_case )
lowercase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(snake_case )
for dataclass_type in dataclass_types:
lowercase = HfArgumentParser(snake_case )
self.argparsersEqual(snake_case , snake_case )
lowercase = parser.parse_args([] )
self.assertEqual(snake_case , Namespace(foo=snake_case , bar=snake_case , baz=snake_case , ces=[] , des=[] ) )
lowercase = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(snake_case , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=snake_case , required=snake_case )
expected.add_argument('--required_str' , type=snake_case , required=snake_case )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=snake_case , )
self.argparsersEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , required=snake_case )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=snake_case , )
expected.add_argument('--opt' , type=snake_case , default=snake_case )
expected.add_argument('--baz' , default='toto' , type=snake_case , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=snake_case )
self.argparsersEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
lowercase = parser.parse_dict(snake_case )[0]
lowercase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 42,
}
self.assertRaises(snake_case , parser.parse_dict , snake_case , allow_extra_keys=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(snake_case , 'temp_json' )
os.mkdir(snake_case )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(snake_case , snake_case )
lowercase = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
lowercase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
lowercase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase = os.path.join(snake_case , 'temp_yaml' )
os.mkdir(snake_case )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(snake_case , snake_case )
lowercase = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
lowercase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = HfArgumentParser(snake_case )
self.assertIsNotNone(snake_case )
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept negative values' )
lowercase = 0
lowercase = str(__SCREAMING_SNAKE_CASE )
while len(__SCREAMING_SNAKE_CASE ) != 1:
lowercase = [int(__SCREAMING_SNAKE_CASE ) for i in num_string]
lowercase = 1
for i in range(0 , len(__SCREAMING_SNAKE_CASE ) ):
total *= numbers[i]
lowercase = str(__SCREAMING_SNAKE_CASE )
steps += 1
return steps
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError('additive_persistence() only accepts integral values' )
if num < 0:
raise ValueError('additive_persistence() does not accept negative values' )
lowercase = 0
lowercase = str(__SCREAMING_SNAKE_CASE )
while len(__SCREAMING_SNAKE_CASE ) != 1:
lowercase = [int(__SCREAMING_SNAKE_CASE ) for i in num_string]
lowercase = 0
for i in range(0 , len(__SCREAMING_SNAKE_CASE ) ):
total += numbers[i]
lowercase = str(__SCREAMING_SNAKE_CASE )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if indegree[i] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
while queue:
lowercase = queue.pop(0 )
cnt += 1
topo.append(__SCREAMING_SNAKE_CASE )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
if cnt != len(__SCREAMING_SNAKE_CASE ):
print('Cycle exists' )
else:
print(__SCREAMING_SNAKE_CASE )
# Adjacency List of Graph
UpperCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 84 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
UpperCAmelCase = logging.get_logger(__name__)
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""input_features""", """attention_mask"""]
def __init__( self , snake_case=80 , snake_case=1_6000 , snake_case=0.0 , snake_case=10 , snake_case=25 , snake_case="hamming_window" , snake_case=32_768.0 , snake_case=0.97 , snake_case=1.0 , snake_case=True , snake_case=True , snake_case=False , **snake_case , ):
super().__init__(feature_size=snake_case , sampling_rate=snake_case , padding_value=snake_case , **snake_case )
lowercase = feature_size
lowercase = sampling_rate
lowercase = padding_value
lowercase = hop_length
lowercase = win_length
lowercase = frame_signal_scale
lowercase = preemphasis_coeff
lowercase = mel_floor
lowercase = normalize_means
lowercase = normalize_vars
lowercase = win_function
lowercase = return_attention_mask
lowercase = win_length * sampling_rate // 1000
lowercase = hop_length * sampling_rate // 1000
lowercase = optimal_fft_length(self.sample_size )
lowercase = (self.n_fft // 2) + 1
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.win_function == "hamming_window":
lowercase = window_function(window_length=self.sample_size , name=self.win_function , periodic=snake_case )
else:
lowercase = window_function(window_length=self.sample_size , name=self.win_function )
lowercase = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
lowercase = spectrogram(
one_waveform * self.frame_signal_scale , window=snake_case , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=snake_case , preemphasis=self.preemphasis_coeff , mel_filters=snake_case , mel_floor=self.mel_floor , log_mel='log' , )
return msfc_features.T
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
# make sure we normalize float32 arrays
if self.normalize_means:
lowercase = x[:input_length].mean(axis=0 )
lowercase = np.subtract(snake_case , snake_case )
if self.normalize_vars:
lowercase = x[:input_length].std(axis=0 )
lowercase = np.divide(snake_case , snake_case )
if input_length < x.shape[0]:
lowercase = padding_value
# make sure array is in float32
lowercase = x.astype(np.floataa )
return x
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(snake_case , snake_case , self.padding_value ) for x, n in zip(snake_case , snake_case )]
def __call__( self , snake_case , snake_case = False , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = None , snake_case = None , **snake_case , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
lowercase = 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}''' )
lowercase = is_batched_numpy or (
isinstance(snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowercase = [np.asarray(snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case , np.ndarray ):
lowercase = np.asarray(snake_case , dtype=np.floataa )
elif isinstance(snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowercase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowercase = [raw_speech]
# extract fbank features
lowercase = [self._extract_mfsc_features(snake_case ) for one_waveform in raw_speech]
# convert into correct format for padding
lowercase = BatchFeature({'input_features': features} )
lowercase = self.pad(
snake_case , padding=snake_case , max_length=snake_case , truncation=snake_case , pad_to_multiple_of=snake_case , return_attention_mask=snake_case , **snake_case , )
# make sure list is in array format
lowercase = padded_inputs.get('input_features' )
if isinstance(input_features[0] , snake_case ):
lowercase = [np.asarray(snake_case , dtype=np.floataa ) for feature in input_features]
lowercase = padded_inputs.get('attention_mask' )
if attention_mask is not None:
lowercase = [np.asarray(snake_case , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowercase = (
np.array(snake_case , dtype=np.intaa )
if self._get_padding_strategies(snake_case , max_length=snake_case ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowercase = self.normalize(
padded_inputs['input_features'] , attention_mask=snake_case )
if return_tensors is not None:
lowercase = padded_inputs.convert_to_tensors(snake_case )
return padded_inputs
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
return (data["data"], data["target"])
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = XGBRegressor(verbosity=0 , random_state=42 )
xgb.fit(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Predict target for test data
lowercase = xgb.predict(__SCREAMING_SNAKE_CASE )
lowercase = predictions.reshape(len(__SCREAMING_SNAKE_CASE ) , 1 )
return predictions
def UpperCAmelCase_ ( ):
lowercase = fetch_california_housing()
lowercase , lowercase = data_handling(__SCREAMING_SNAKE_CASE )
lowercase , lowercase , lowercase , lowercase = train_test_split(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , test_size=0.25 , random_state=1 )
lowercase = xgboost(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}''' )
print(F'''Mean Square Error : {mean_squared_error(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 84 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 | 1 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class A_ :
'''simple docstring'''
def __init__( self ):
lowercase = ''
lowercase = ''
lowercase = []
lowercase = 0
lowercase = 256
lowercase = 0
lowercase = 0
lowercase = 0
lowercase = 0
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = cva.imread(snake_case , 0 )
lowercase = copy.deepcopy(self.img )
lowercase , lowercase , lowercase = plt.hist(self.img.ravel() , 256 , [0, 256] , label='x' )
lowercase = np.sum(snake_case )
for i in range(len(snake_case ) ):
lowercase = x[i] / self.k
self.sk += prk
lowercase = (self.L - 1) * self.sk
if self.rem != 0:
lowercase = int(last % last )
lowercase = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(snake_case )
lowercase = int(np.ma.count(self.img ) / self.img[1].size )
lowercase = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
lowercase = self.img[j][i]
if num != self.last_list[num]:
lowercase = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def SCREAMING_SNAKE_CASE__ ( self ):
plt.hist(self.img.ravel() , 256 , [0, 256] )
def SCREAMING_SNAKE_CASE__ ( self ):
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
UpperCAmelCase = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
UpperCAmelCase = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 50 ):
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 84 |
import unittest
from transformers import 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 (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTDoubleHeadsModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = self.num_labels
lowercase = OpenAIGPTForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCamelCase : str = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=False ):
lowercase = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=snake_case , )
lowercase = inputs_dict['labels']
lowercase = inputs_dict['labels']
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=snake_case , )
lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = OpenAIGPTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(snake_case )
lowercase = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=snake_case ) # the president is
lowercase = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 84 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = """data2vec-text"""
def __init__( self , snake_case=3_0522 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=1 , snake_case=0 , snake_case=2 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ):
super().__init__(pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = position_embedding_type
lowercase = use_cache
lowercase = classifier_dropout
class A_ ( __lowerCamelCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.task == "multiple-choice":
lowercase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
lowercase = False
lowercase = index + i
lowercase = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 9999_6666_3333 ):
lowercase = math.floor(math.sqrt(__SCREAMING_SNAKE_CASE ) ) + 100
lowercase = prime_sieve(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = primes[prime_index]
while (last_prime**2) <= limit:
lowercase = primes[prime_index + 1]
lowercase = last_prime**2
lowercase = next_prime**2
# Get numbers divisible by lps(current)
lowercase = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowercase = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowercase = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowercase = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 84 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase = logging.getLogger(__name__)
class A_ :
'''simple docstring'''
def __init__( self ):
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if not self.initialized:
lowercase = RagRetriever(
snake_case , question_encoder_tokenizer=snake_case , generator_tokenizer=snake_case , index=snake_case , init_retrieval=snake_case , )
lowercase = True
def SCREAMING_SNAKE_CASE__ ( self ):
self.retriever.index.init_index()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase , lowercase = self.retriever._main_retrieve(snake_case , snake_case )
return doc_ids, retrieved_doc_embeds
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=None ):
if index is not None and index.is_initialized() and len(snake_case ) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
super().__init__(
snake_case , question_encoder_tokenizer=snake_case , generator_tokenizer=snake_case , index=snake_case , init_retrieval=snake_case , )
lowercase = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(snake_case , snake_case , snake_case , snake_case )
for worker in self.retrieval_workers
] )
def SCREAMING_SNAKE_CASE__ ( self ):
logger.info('initializing retrieval' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
lowercase = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
lowercase , lowercase = ray.get(random_worker.retrieve.remote(snake_case , snake_case ) )
else:
lowercase , lowercase = self._main_retrieve(snake_case , snake_case )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case=None , **snake_case ):
return super(snake_case , cls ).get_tokenizers(snake_case , snake_case , **snake_case )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case , snake_case=None , **snake_case ):
lowercase = kwargs.pop('config' , snake_case ) or RagConfig.from_pretrained(snake_case , **snake_case )
lowercase = RagTokenizer.from_pretrained(snake_case , config=snake_case )
lowercase = rag_tokenizer.question_encoder
lowercase = rag_tokenizer.generator
if indexed_dataset is not None:
lowercase = 'custom'
lowercase = CustomHFIndex(config.retrieval_vector_size , snake_case )
else:
lowercase = cls._build_index(snake_case )
return cls(
snake_case , question_encoder_tokenizer=snake_case , generator_tokenizer=snake_case , retrieval_workers=snake_case , index=snake_case , )
| 84 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
UpperCAmelCase = re.compile(R'''^\s*try:''')
# Catches a line with else:
UpperCAmelCase = re.compile(R'''^\s*else:''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if _re_test_backend.search(__SCREAMING_SNAKE_CASE ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ):
lowercase = _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ).groups()[0]
lowercase = re.findall(r'\[([^\]]+)\]' , __SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_between_brackets.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
def find_duplicates(__SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def UpperCAmelCase_ ( ):
lowercase = []
for root, _, files in os.walk(__SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' )
lowercase = parse_init(__SCREAMING_SNAKE_CASE )
if objects is not None:
lowercase = analyze_results(*__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = []
for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / folder).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace(os.path.sep , '.' )
submodules.append(__SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / fname).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__SCREAMING_SNAKE_CASE )
return submodules
UpperCAmelCase = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def UpperCAmelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
lowercase = direct_transformers_import(__SCREAMING_SNAKE_CASE )
lowercase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
lowercase = f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , __SCREAMING_SNAKE_CASE ) ) )
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 84 | 1 |
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case ):
lowercase = name
lowercase = val
def __str__( self ):
return F'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self , snake_case ):
return self.val < other.val
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = {}
lowercase = {}
lowercase = self.build_heap(snake_case )
def __getitem__( self , snake_case ):
return self.get_value(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return (idx - 1) // 2
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return idx * 2 + 1
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return idx * 2 + 2
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.heap_dict[key]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = len(snake_case ) - 1
lowercase = self.get_parent_idx(snake_case )
for idx, i in enumerate(snake_case ):
lowercase = idx
lowercase = i.val
for i in range(snake_case , -1 , -1 ):
self.sift_down(snake_case , snake_case )
return array
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
while True:
lowercase = self.get_left_child_idx(snake_case ) # noqa: E741
lowercase = self.get_right_child_idx(snake_case )
lowercase = idx
if l < len(snake_case ) and array[l] < array[idx]:
lowercase = l
if r < len(snake_case ) and array[r] < array[smallest]:
lowercase = r
if smallest != idx:
lowercase , lowercase = array[smallest], array[idx]
(
(
lowercase
) , (
lowercase
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
lowercase = smallest
else:
break
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.get_parent_idx(snake_case )
while p >= 0 and self.heap[p] > self.heap[idx]:
lowercase , lowercase = self.heap[idx], self.heap[p]
lowercase , lowercase = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
lowercase = p
lowercase = self.get_parent_idx(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.heap[0]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.heap[-1], self.heap[0]
lowercase , lowercase = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
lowercase = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
self.heap.append(snake_case )
lowercase = len(self.heap ) - 1
lowercase = node.val
self.sift_up(len(self.heap ) - 1 )
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.heap ) == 0
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
lowercase = new_value
lowercase = new_value
self.sift_up(self.idx_of_element[node] )
UpperCAmelCase = Node('''R''', -1)
UpperCAmelCase = Node('''B''', 6)
UpperCAmelCase = Node('''A''', 3)
UpperCAmelCase = Node('''X''', 1)
UpperCAmelCase = Node('''E''', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
UpperCAmelCase = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
print(i)
print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = None
def __str__( self ):
return F'''{self.data}'''
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
lowercase = None
def __iter__( self ):
lowercase = self.top
while node:
yield node.data
lowercase = node.next
def __str__( self ):
return "->".join([str(snake_case ) for item in self] )
def __len__( self ):
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.top is None
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = Node(snake_case )
if not self.is_empty():
lowercase = self.top
lowercase = node
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case )
lowercase = self.top
lowercase = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = inspect.getfile(accelerate.test_utils )
lowercase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
lowercase = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_distributed_data_loop.py'] )
lowercase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_ops.py'] )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(F'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='0,1' ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
if __name__ == "__main__":
UpperCAmelCase = Accelerator()
UpperCAmelCase = (accelerator.state.process_index + 2, 10)
UpperCAmelCase = torch.randint(0, 10, shape).to(accelerator.device)
UpperCAmelCase = ''''''
UpperCAmelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
UpperCAmelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
UpperCAmelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 84 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__SCREAMING_SNAKE_CASE )
if number < 1:
lowercase = F'''Input value of [number={number}] must be > 0'''
raise ValueError(__SCREAMING_SNAKE_CASE )
lowercase = 1
for i in range(1 , __SCREAMING_SNAKE_CASE ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : Dict = """dummy_data"""
_UpperCamelCase : Optional[int] = """datasets"""
_UpperCamelCase : Tuple = False
def __init__( self , snake_case , snake_case , snake_case , snake_case = None , snake_case = False , snake_case = True , snake_case = None , ):
lowercase = 0
lowercase = dataset_name
lowercase = cache_dir
lowercase = use_local_dummy_data
lowercase = config
# download_callbacks take a single url as input
lowercase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase = str(snake_case )
# to be downloaded
lowercase = None
lowercase = None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._dummy_file is None:
lowercase = self.download_dummy_data()
return self._dummy_file
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase = cached_path(
snake_case , cache_dir=self.cache_dir , extract_compressed_file=snake_case , force_extract=snake_case )
return os.path.join(snake_case , self.dummy_file_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._bucket_url is None:
lowercase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def SCREAMING_SNAKE_CASE__ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case , snake_case ):
return self.create_dummy_data_dict(snake_case , snake_case )
elif isinstance(snake_case , (list, tuple) ):
return self.create_dummy_data_list(snake_case , snake_case )
else:
return self.create_dummy_data_single(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case , **snake_case ):
return path
def SCREAMING_SNAKE_CASE__ ( self ):
return {}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case , snake_case ):
for single_url in single_urls:
download_callback(snake_case )
else:
lowercase = single_urls
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(snake_case , snake_case ):
lowercase = [os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) ) for x in single_urls]
else:
lowercase = single_urls
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) )
lowercase = value
# make sure that values are unique
if all(isinstance(snake_case , snake_case ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , snake_case ) ) for url in data_url )
lowercase = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase = [data_url[0]] * len(snake_case )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(snake_case )
return dummy_data_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(snake_case ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
def _iter_archive_members(snake_case ):
# this preserves the order of the members inside the ZIP archive
lowercase = Path(self.dummy_file ).parent
lowercase = path.relative_to(snake_case )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case )
lowercase = Path(snake_case )
lowercase = _iter_archive_members(snake_case ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(snake_case ).as_posix(), file_path.open('rb' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
lowercase = [paths]
for path in paths:
if os.path.isfile(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(snake_case ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(snake_case , snake_case )
| 84 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for attribute in key.split('.' ):
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if weight_type is not None:
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).shape
else:
lowercase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowercase = value
elif weight_type == "weight_g":
lowercase = value
elif weight_type == "weight_v":
lowercase = value
elif weight_type == "bias":
lowercase = value
elif weight_type == "running_mean":
lowercase = value
elif weight_type == "running_var":
lowercase = value
elif weight_type == "num_batches_tracked":
lowercase = value
elif weight_type == "inv_freq":
lowercase = value
else:
lowercase = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
lowercase = fairseq_model.state_dict()
lowercase = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowercase = False
if "conv_layers" in name:
load_conv_layer(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
lowercase = True
else:
for key, mapped_key in MAPPING.items():
lowercase = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
lowercase = True
if "*" in mapped_key:
lowercase = name.split(__SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
lowercase = mapped_key.replace('*' , __SCREAMING_SNAKE_CASE )
if "pos_bias_u" in name:
lowercase = None
elif "pos_bias_v" in name:
lowercase = None
elif "weight_g" in name:
lowercase = 'weight_g'
elif "weight_v" in name:
lowercase = 'weight_v'
elif "bias" in name:
lowercase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowercase = 'weight'
elif "running_mean" in name:
lowercase = 'running_mean'
elif "inv_freq" in name:
lowercase = 'inv_freq'
elif "running_var" in name:
lowercase = 'running_var'
elif "num_batches_tracked" in name:
lowercase = 'num_batches_tracked'
else:
lowercase = None
set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(__SCREAMING_SNAKE_CASE )
logger.warning(F'''Unused weights: {unused_weights}''' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = full_name.split('conv_layers.' )[-1]
lowercase = name.split('.' )
lowercase = int(items[0] )
lowercase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
lowercase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__SCREAMING_SNAKE_CASE )
@torch.no_grad()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=True ):
if config_path is not None:
lowercase = WavaVecaConformerConfig.from_pretrained(__SCREAMING_SNAKE_CASE , hidden_act='swish' )
else:
lowercase = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowercase = 'rotary'
if is_finetuned:
if dict_path:
lowercase = Dictionary.load(__SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowercase = target_dict.pad_index
lowercase = target_dict.bos_index
lowercase = target_dict.eos_index
lowercase = len(target_dict.symbols )
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , 'vocab.json' )
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE ) )
return
os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
lowercase = target_dict.indices
# fairseq has the <pad> and <s> switched
lowercase = 0
lowercase = 1
with open(__SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = WavaVecaCTCTokenizer(
__SCREAMING_SNAKE_CASE , 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=__SCREAMING_SNAKE_CASE , )
lowercase = True if config.feat_extract_norm == 'layer' else False
lowercase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )
lowercase = WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
lowercase = WavaVecaConformerForCTC(__SCREAMING_SNAKE_CASE )
else:
lowercase = WavaVecaConformerForPreTraining(__SCREAMING_SNAKE_CASE )
if is_finetuned:
lowercase , lowercase , lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
lowercase = argparse.Namespace(task='audio_pretraining' )
lowercase = fairseq.tasks.setup_task(__SCREAMING_SNAKE_CASE )
lowercase , lowercase , lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__SCREAMING_SNAKE_CASE )
lowercase = model[0].eval()
recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , not is_finetuned )
hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
UpperCAmelCase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 84 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('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 SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class A_ :
'''simple docstring'''
_UpperCamelCase : str = BlenderbotConfig
_UpperCamelCase : Tuple = {}
_UpperCamelCase : List[Any] = """gelu"""
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=False , snake_case=99 , snake_case=32 , snake_case=2 , snake_case=4 , snake_case=37 , snake_case=0.1 , snake_case=0.1 , snake_case=20 , snake_case=2 , snake_case=1 , snake_case=0 , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = eos_token_id
lowercase = pad_token_id
lowercase = bos_token_id
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowercase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowercase = tf.concat([input_ids, eos_tensor] , axis=1 )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase = prepare_blenderbot_inputs_dict(snake_case , snake_case , snake_case )
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = TFBlenderbotModel(config=snake_case ).get_decoder()
lowercase = inputs_dict['input_ids']
lowercase = input_ids[:1, :]
lowercase = inputs_dict['attention_mask'][:1, :]
lowercase = inputs_dict['head_mask']
lowercase = 1
# first forward pass
lowercase = model(snake_case , attention_mask=snake_case , head_mask=snake_case , use_cache=snake_case )
lowercase , lowercase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowercase = tf.concat([input_ids, next_tokens] , axis=-1 )
lowercase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowercase = model(snake_case , attention_mask=snake_case )[0]
lowercase = model(snake_case , attention_mask=snake_case , past_key_values=snake_case )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowercase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowercase = output_from_no_past[:, -3:, random_slice_idx]
lowercase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(snake_case , snake_case , rtol=1E-3 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , ):
if attention_mask is None:
lowercase = tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowercase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowercase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class A_ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
_UpperCamelCase : Optional[int] = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
_UpperCamelCase : List[str] = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
_UpperCamelCase : Tuple = True
_UpperCamelCase : List[str] = False
_UpperCamelCase : Dict = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFBlenderbotModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*snake_case )
@require_tokenizers
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Any = ["""My friends are cool but they eat too many carbs."""]
_UpperCamelCase : List[Any] = """facebook/blenderbot-400M-distill"""
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.tokenizer(self.src_text , return_tensors='tf' )
lowercase = self.model.generate(
model_inputs.input_ids , )
lowercase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=snake_case )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 84 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_terminal_summary_main
lowercase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE , id=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase = 0
# Doctest custom flag to ignore output.
UpperCAmelCase = doctest.register_optionflag('''IGNORE_RESULT''')
UpperCAmelCase = doctest.OutputChecker
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , snake_case , snake_case , snake_case )
UpperCAmelCase = CustomOutputChecker
UpperCAmelCase = HfDoctestModule
UpperCAmelCase = HfDocTestParser
| 84 | 1 |
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = x
lowercase = y
for step in range(__SCREAMING_SNAKE_CASE ): # noqa: B007
lowercase = a * a - b * b + x
lowercase = 2 * a * b + y
lowercase = 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_ ( __SCREAMING_SNAKE_CASE ):
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(__SCREAMING_SNAKE_CASE , 1 , 1 ) )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 800 , __SCREAMING_SNAKE_CASE = 600 , __SCREAMING_SNAKE_CASE = -0.6 , __SCREAMING_SNAKE_CASE = 0 , __SCREAMING_SNAKE_CASE = 3.2 , __SCREAMING_SNAKE_CASE = 50 , __SCREAMING_SNAKE_CASE = True , ):
lowercase = Image.new('RGB' , (image_width, image_height) )
lowercase = img.load()
# loop through the image-coordinates
for image_x in range(__SCREAMING_SNAKE_CASE ):
for image_y in range(__SCREAMING_SNAKE_CASE ):
# determine the figure-coordinates based on the image-coordinates
lowercase = figure_width / image_width * image_height
lowercase = figure_center_x + (image_x / image_width - 0.5) * figure_width
lowercase = figure_center_y + (image_y / image_height - 0.5) * figure_height
lowercase = get_distance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
lowercase = get_color_coded_rgb(__SCREAMING_SNAKE_CASE )
else:
lowercase = get_black_and_white_rgb(__SCREAMING_SNAKE_CASE )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
UpperCAmelCase = 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()
| 84 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase = n_token
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [n_token]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowercase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowercase = nn.Parameter(torch.zeros(self.n_clusters ) )
lowercase = nn.ModuleList()
lowercase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
else:
self.out_projs.append(snake_case )
self.out_layers.append(nn.Linear(snake_case , snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
self.out_layers.append(nn.Linear(snake_case , r_idx - l_idx ) )
lowercase = keep_order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if proj is None:
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowercase = nn.functional.linear(snake_case , proj.t().contiguous() )
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None , snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
lowercase = hidden[..., :-1, :].contiguous()
lowercase = labels[..., 1:].contiguous()
lowercase = hidden.view(-1 , hidden.size(-1 ) )
lowercase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
lowercase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowercase = labels != -100
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = (
-nn.functional.log_softmax(snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowercase = nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
if labels is None:
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = 0
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowercase = (labels >= l_idx) & (labels < r_idx)
lowercase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowercase = labels.index_select(0 , snake_case ) - l_idx
lowercase = head_logprob.index_select(0 , snake_case )
lowercase = hidden.index_select(0 , snake_case )
else:
lowercase = hidden
if i == 0:
if labels is not None:
lowercase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowercase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowercase = logprob_i
if labels is not None:
if (hasattr(self , 'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 , snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = head_logprob[:, -i] + tail_logprob_i
lowercase = logprob_i
return out
| 84 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
lowercase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE , id=__SCREAMING_SNAKE_CASE )
| 84 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.' )
if len(snake_case ) != 0:
lowercase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(snake_case ) != cols:
raise error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise error
lowercase = rows
else:
lowercase = []
def SCREAMING_SNAKE_CASE__ ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows[0] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return (self.num_rows, self.num_columns)
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.order[0] == self.order[1]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return bool(self.determinant() )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(snake_case ).determinant()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if (row + column) % 2 == 0:
return self.get_minor(snake_case , snake_case )
return -1 * self.get_minor(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[self.get_minor(snake_case , snake_case ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
for row in self.rows
] )
+ "]"
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError('Row must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix' )
if position is None:
self.rows.append(snake_case )
else:
lowercase = self.rows[0:position] + [row] + self.rows[position:]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError(
'Column must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in column:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix' )
if position is None:
lowercase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , snake_case ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , snake_case ):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , snake_case ):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , snake_case ):
if isinstance(snake_case , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(snake_case , snake_case ):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second' )
return Matrix(
[
[Matrix.dot_product(snake_case , snake_case ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix' )
def __pow__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise TypeError('A Matrix can only be raised to the power of an int' )
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power' )
lowercase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case ):
return sum(row[i] * column[i] for i in range(len(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1_6000 ):
lowercase = int(round(sample_rate * max_length ) )
if len(__SCREAMING_SNAKE_CASE ) <= sample_length:
return wav
lowercase = randint(0 , len(__SCREAMING_SNAKE_CASE ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : Optional[str] = field(default=__lowerCamelCase , metadata={"""help""": """Name of a dataset from the datasets package"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """A file containing the training audio paths and labels."""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""} )
_UpperCamelCase : str = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
_UpperCamelCase : str = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
_UpperCamelCase : str = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
_UpperCamelCase : str = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""} )
_UpperCamelCase : Optional[int] = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_UpperCamelCase : Optional[int] = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_UpperCamelCase : float = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class A_ :
'''simple docstring'''
_UpperCamelCase : str = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""} )
_UpperCamelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_UpperCamelCase : Optional[str] = field(
default=__lowerCamelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_UpperCamelCase : Optional[bool] = field(
default=__lowerCamelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'The argument `--freeze_feature_extractor` is deprecated and '
'will be removed in a future version. Use `--freeze_feature_encoder`'
'instead. Setting `freeze_feature_encoder==True`.' , snake_case , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'The argument `--freeze_feature_extractor` is deprecated and '
'should not be used in combination with `--freeze_feature_encoder`.'
'Only make use of `--freeze_feature_encoder`.' )
def UpperCAmelCase_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_audio_classification' , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase = training_args.get_process_log_level()
logger.setLevel(__SCREAMING_SNAKE_CASE )
transformers.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to train from scratch.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset and prepare it for the audio classification task.
lowercase = DatasetDict()
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
'Make sure to set `--audio_column_name` to the correct audio column - one of '
F'''{', '.join(raw_datasets['train'].column_names )}.''' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
'Make sure to set `--label_column_name` to the correct text column - one of '
F'''{', '.join(raw_datasets['train'].column_names )}.''' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase = feature_extractor.model_input_names[0]
def train_transforms(__SCREAMING_SNAKE_CASE ):
lowercase = []
for audio in batch[data_args.audio_column_name]:
lowercase = random_subsample(
audio['array'] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__SCREAMING_SNAKE_CASE )
lowercase = feature_extractor(__SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(__SCREAMING_SNAKE_CASE )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(__SCREAMING_SNAKE_CASE ):
lowercase = [audio['array'] for audio in batch[data_args.audio_column_name]]
lowercase = feature_extractor(__SCREAMING_SNAKE_CASE , sampling_rate=feature_extractor.sampling_rate )
lowercase = {model_input_name: inputs.get(__SCREAMING_SNAKE_CASE )}
lowercase = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase = raw_datasets['train'].features[data_args.label_column_name].names
lowercase , lowercase = {}, {}
for i, label in enumerate(__SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
lowercase = label
# Load the accuracy metric from the datasets package
lowercase = evaluate.load('accuracy' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(__SCREAMING_SNAKE_CASE ):
lowercase = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=__SCREAMING_SNAKE_CASE , references=eval_pred.label_ids )
lowercase = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__SCREAMING_SNAKE_CASE ) , labelaid=__SCREAMING_SNAKE_CASE , idalabel=__SCREAMING_SNAKE_CASE , finetuning_task='audio-classification' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=__SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase = (
raw_datasets['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__SCREAMING_SNAKE_CASE , output_all_columns=__SCREAMING_SNAKE_CASE )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase = (
raw_datasets['eval'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__SCREAMING_SNAKE_CASE , output_all_columns=__SCREAMING_SNAKE_CASE )
# Initialize our trainer
lowercase = Trainer(
model=__SCREAMING_SNAKE_CASE , args=__SCREAMING_SNAKE_CASE , train_dataset=raw_datasets['train'] if training_args.do_train else None , eval_dataset=raw_datasets['eval'] if training_args.do_eval else None , compute_metrics=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
lowercase = None
if training_args.resume_from_checkpoint is not None:
lowercase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase = last_checkpoint
lowercase = trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase = trainer.evaluate()
trainer.log_metrics('eval' , __SCREAMING_SNAKE_CASE )
trainer.save_metrics('eval' , __SCREAMING_SNAKE_CASE )
# Write model card and (optionally) push to hub
lowercase = {
'finetuned_from': model_args.model_name_or_path,
'tasks': 'audio-classification',
'dataset': data_args.dataset_name,
'tags': ['audio-classification'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__SCREAMING_SNAKE_CASE )
else:
trainer.create_model_card(**__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 84 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=8 ):
lowercase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , ):
super().__init__()
self.register_modules(
unet=snake_case , scheduler=snake_case , movq=snake_case , )
lowercase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
if latents is None:
lowercase = randn_tensor(snake_case , generator=snake_case , device=snake_case , dtype=snake_case )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase = latents.to(snake_case )
lowercase = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
lowercase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=snake_case )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase , lowercase = cpu_offload_with_hook(snake_case , snake_case , prev_module_hook=snake_case )
# We'll offload the last model manually.
lowercase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE__ ( self ):
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(snake_case , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(snake_case )
def __call__( self , snake_case , snake_case , snake_case = 512 , snake_case = 512 , snake_case = 100 , snake_case = 4.0 , snake_case = 1 , snake_case = None , snake_case = None , snake_case = "pil" , snake_case = True , ):
lowercase = self._execution_device
lowercase = guidance_scale > 1.0
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
lowercase = image_embeds.shape[0] * num_images_per_prompt
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
if do_classifier_free_guidance:
lowercase = image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = negative_image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=snake_case )
self.scheduler.set_timesteps(snake_case , device=snake_case )
lowercase = self.scheduler.timesteps
lowercase = self.unet.config.in_channels
lowercase , lowercase = downscale_height_and_width(snake_case , snake_case , self.movq_scale_factor )
# create initial latent
lowercase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , snake_case , snake_case , snake_case , self.scheduler , )
for i, t in enumerate(self.progress_bar(snake_case ) ):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase = {'image_embeds': image_embeds}
lowercase = self.unet(
sample=snake_case , timestep=snake_case , encoder_hidden_states=snake_case , added_cond_kwargs=snake_case , return_dict=snake_case , )[0]
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
lowercase , lowercase = noise_pred.chunk(2 )
lowercase , lowercase = variance_pred.chunk(2 )
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(
snake_case , snake_case , snake_case , generator=snake_case , )[0]
# post-processing
lowercase = self.movq.decode(snake_case , force_not_quantize=snake_case )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase = image * 0.5 + 0.5
lowercase = image.clamp(0 , 1 )
lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case )
| 84 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = """Salesforce/blip-image-captioning-base"""
_UpperCamelCase : List[Any] = (
"""This is a tool that generates a description of an image. It takes an input named `image` which should be the """
"""image to caption, and returns a text that contains the description in English."""
)
_UpperCamelCase : Union[str, Any] = """image_captioner"""
_UpperCamelCase : int = AutoModelForVisionaSeq
_UpperCamelCase : Optional[Any] = ["""image"""]
_UpperCamelCase : str = ["""text"""]
def __init__( self , *snake_case , **snake_case ):
requires_backends(self , ['vision'] )
super().__init__(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.pre_processor(images=snake_case , return_tensors='pt' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.model.generate(**snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.pre_processor.batch_decode(snake_case , skip_special_tokens=snake_case )[0].strip()
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase = FlaxAutoModelForSeqaSeqLM.from_config(config=__SCREAMING_SNAKE_CASE )
lowercase = checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE )
lowercase = 'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']
if config.model_type == "t5":
lowercase = 'SelfAttention'
if config.model_type == "longt5" and config.encoder_attention_type == "local":
lowercase = 'LocalSelfAttention'
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowercase = 'TransientGlobalSelfAttention'
else:
raise ValueError(
'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
' attribute with a value from [\'local\', \'transient-global].' )
# Encoder
for layer_index in range(config.num_layers ):
lowercase = F'''layers_{str(__SCREAMING_SNAKE_CASE )}'''
# Self-Attention
lowercase = tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
lowercase = tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
lowercase = tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
lowercase = tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowercase = tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']
# Layer Normalization
lowercase = tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']
if split_mlp_wi:
lowercase = tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
lowercase = tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowercase = tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']
lowercase = tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowercase = tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowercase = flax_model.params['encoder']['block'][str(__SCREAMING_SNAKE_CASE )]['layer']
lowercase = tax_attention_key
lowercase = tax_attention_out
lowercase = tax_attention_query
lowercase = tax_attention_value
lowercase = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowercase = tax_global_layer_norm
if split_mlp_wi:
lowercase = tax_mlp_wi_a
lowercase = tax_mlp_wi_a
else:
lowercase = tax_mlp_wi
lowercase = tax_mlp_wo
lowercase = tax_mlp_layer_norm
lowercase = flax_model_encoder_layer_block
# Only for layer 0:
lowercase = tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
lowercase = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
lowercase = tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
lowercase = tax_encoder_global_rel_embedding
# Assigning
lowercase = tax_model['target']['encoder']['encoder_norm']['scale']
lowercase = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
lowercase = F'''layers_{str(__SCREAMING_SNAKE_CASE )}'''
# Self-Attention
lowercase = tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
lowercase = tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
lowercase = tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
lowercase = tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']
# Layer Normalization
lowercase = tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
'scale'
]
# Encoder-Decoder-Attention
lowercase = tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
lowercase = tax_enc_dec_attention_module['key']['kernel']
lowercase = tax_enc_dec_attention_module['out']['kernel']
lowercase = tax_enc_dec_attention_module['query']['kernel']
lowercase = tax_enc_dec_attention_module['value']['kernel']
# Layer Normalization
lowercase = tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']
# MLP
if split_mlp_wi:
lowercase = tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
lowercase = tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
else:
lowercase = tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']
lowercase = tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']
# Layer Normalization
lowercase = tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']
# Assigning
lowercase = flax_model.params['decoder']['block'][str(__SCREAMING_SNAKE_CASE )]['layer']
lowercase = tax_attention_key
lowercase = tax_attention_out
lowercase = tax_attention_query
lowercase = tax_attention_value
lowercase = tax_pre_attention_layer_norm
lowercase = tax_enc_dec_attention_key
lowercase = tax_enc_dec_attention_out
lowercase = tax_enc_dec_attention_query
lowercase = tax_enc_dec_attention_value
lowercase = tax_cross_layer_norm
if split_mlp_wi:
lowercase = tax_mlp_wi_a
lowercase = tax_mlp_wi_a
else:
lowercase = tax_mlp_wi
lowercase = tax_mlp_wo
lowercase = txa_mlp_layer_norm
lowercase = flax_model_decoder_layer_block
# Decoder Normalization
lowercase = tax_model['target']['decoder']['decoder_norm']['scale']
lowercase = txa_decoder_norm
# Only for layer 0:
lowercase = tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
lowercase = tax_decoder_rel_embedding
# Token Embeddings
lowercase = tax_model['target']['token_embedder']['embedding']
lowercase = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
lowercase = tax_model['target']['decoder']['logits_dense']['kernel']
flax_model.save_pretrained(__SCREAMING_SNAKE_CASE )
print('T5X Model was sucessfully converted!' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
)
parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
parser.add_argument(
'''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
)
UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 84 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCREAMING_SNAKE_CASE ):
if is_palindrome(__SCREAMING_SNAKE_CASE ) and is_palindrome(bin(__SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 84 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = args.log_outputs
lowercase = '_'.join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
lowercase = load_metric('wer' )
lowercase = load_metric('cer' )
# compute metrics
lowercase = wer.compute(references=result['target'] , predictions=result['prediction'] )
lowercase = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
lowercase = F'''WER: {wer_result}\nCER: {cer_result}'''
print(__SCREAMING_SNAKE_CASE )
with open(F'''{dataset_id}_eval_results.txt''' , 'w' ) as f:
f.write(__SCREAMING_SNAKE_CASE )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
lowercase = F'''log_{dataset_id}_predictions.txt'''
lowercase = F'''log_{dataset_id}_targets.txt'''
with open(__SCREAMING_SNAKE_CASE , 'w' ) as p, open(__SCREAMING_SNAKE_CASE , 'w' ) as t:
# mapping function to write output
def write_to_file(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
p.write(F'''{i}''' + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(F'''{i}''' + '\n' )
t.write(batch['target'] + '\n' )
result.map(__SCREAMING_SNAKE_CASE , with_indices=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
lowercase = re.sub(__SCREAMING_SNAKE_CASE , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
lowercase = ['\n\n', '\n', ' ', ' ']
for t in token_sequences_to_ignore:
lowercase = ' '.join(text.split(__SCREAMING_SNAKE_CASE ) )
return text
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# load dataset
lowercase = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__SCREAMING_SNAKE_CASE )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
lowercase = AutoFeatureExtractor.from_pretrained(args.model_id )
lowercase = feature_extractor.sampling_rate
# resample audio
lowercase = dataset.cast_column('audio' , Audio(sampling_rate=__SCREAMING_SNAKE_CASE ) )
# load eval pipeline
if args.device is None:
lowercase = 0 if torch.cuda.is_available() else -1
lowercase = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__SCREAMING_SNAKE_CASE ):
lowercase = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
lowercase = prediction['text']
lowercase = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
lowercase = dataset.map(__SCREAMING_SNAKE_CASE , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
UpperCAmelCase = parser.parse_args()
main(args)
| 84 |
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """conditional_detr"""
_UpperCamelCase : Any = ["""past_key_values"""]
_UpperCamelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , snake_case=True , snake_case=None , snake_case=3 , snake_case=300 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case="relu" , snake_case=256 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1.0 , snake_case=False , snake_case="sine" , snake_case="resnet50" , snake_case=True , snake_case=False , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=1 , snake_case=1 , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=0.25 , **snake_case , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case , snake_case ):
lowercase = backbone_config.get('model_type' )
lowercase = CONFIG_MAPPING[backbone_model_type]
lowercase = config_class.from_dict(snake_case )
lowercase = use_timm_backbone
lowercase = backbone_config
lowercase = num_channels
lowercase = num_queries
lowercase = d_model
lowercase = encoder_ffn_dim
lowercase = encoder_layers
lowercase = encoder_attention_heads
lowercase = decoder_ffn_dim
lowercase = decoder_layers
lowercase = decoder_attention_heads
lowercase = dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = activation_function
lowercase = init_std
lowercase = init_xavier_std
lowercase = encoder_layerdrop
lowercase = decoder_layerdrop
lowercase = encoder_layers
lowercase = auxiliary_loss
lowercase = position_embedding_type
lowercase = backbone
lowercase = use_pretrained_backbone
lowercase = dilation
# Hungarian matcher
lowercase = class_cost
lowercase = bbox_cost
lowercase = giou_cost
# Loss coefficients
lowercase = mask_loss_coefficient
lowercase = dice_loss_coefficient
lowercase = cls_loss_coefficient
lowercase = bbox_loss_coefficient
lowercase = giou_loss_coefficient
lowercase = focal_alpha
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.d_model
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 12
| 84 | 1 |
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase = '''docs/source/en/_toctree.yml'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = defaultdict(__SCREAMING_SNAKE_CASE )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase = [key for key, value in counts.items() if value > 1]
lowercase = []
for duplicate_key in duplicates:
lowercase = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(__SCREAMING_SNAKE_CASE ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE : s["title"].lower() )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE=False ):
with open(__SCREAMING_SNAKE_CASE , encoding='utf-8' ) as f:
lowercase = yaml.safe_load(f.read() )
# Get to the API doc
lowercase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase = content[api_idx]['sections']
# Then to the model doc
lowercase = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase = api_doc[model_idx]['sections']
lowercase = [(idx, section) for idx, section in enumerate(__SCREAMING_SNAKE_CASE ) if 'sections' in section]
lowercase = False
for idx, modality_doc in modalities_docs:
lowercase = modality_doc['sections']
lowercase = clean_model_doc_toc(__SCREAMING_SNAKE_CASE )
if old_modality_doc != new_modality_doc:
lowercase = True
if overwrite:
lowercase = new_modality_doc
if diff:
if overwrite:
lowercase = model_doc
lowercase = api_doc
with open(__SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__SCREAMING_SNAKE_CASE , allow_unicode=__SCREAMING_SNAKE_CASE ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
UpperCAmelCase = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (EulerDiscreteScheduler,)
_UpperCamelCase : Tuple = 10
def SCREAMING_SNAKE_CASE__ ( self , **snake_case ):
lowercase = {
'num_train_timesteps': 1100,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**snake_case )
return config
def SCREAMING_SNAKE_CASE__ ( self ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=snake_case , beta_end=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps )
lowercase = torch.manual_seed(0 )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase = sample.to(snake_case )
for i, t in enumerate(scheduler.timesteps ):
lowercase = scheduler.scale_model_input(snake_case , snake_case )
lowercase = model(snake_case , snake_case )
lowercase = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
lowercase = output.prev_sample
lowercase = torch.sum(torch.abs(snake_case ) )
lowercase = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config(prediction_type='v_prediction' )
lowercase = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps )
lowercase = torch.manual_seed(0 )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma
lowercase = sample.to(snake_case )
for i, t in enumerate(scheduler.timesteps ):
lowercase = scheduler.scale_model_input(snake_case , snake_case )
lowercase = model(snake_case , snake_case )
lowercase = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
lowercase = output.prev_sample
lowercase = torch.sum(torch.abs(snake_case ) )
lowercase = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 0.0_002 ) < 1E-2
assert abs(result_mean.item() - 2.2_676E-06 ) < 1E-3
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case )
lowercase = torch.manual_seed(0 )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowercase = sample.to(snake_case )
for t in scheduler.timesteps:
lowercase = scheduler.scale_model_input(snake_case , snake_case )
lowercase = model(snake_case , snake_case )
lowercase = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
lowercase = output.prev_sample
lowercase = torch.sum(torch.abs(snake_case ) )
lowercase = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.scheduler_classes[0]
lowercase = self.get_scheduler_config()
lowercase = scheduler_class(**snake_case , use_karras_sigmas=snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=snake_case )
lowercase = torch.manual_seed(0 )
lowercase = self.dummy_model()
lowercase = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowercase = sample.to(snake_case )
for t in scheduler.timesteps:
lowercase = scheduler.scale_model_input(snake_case , snake_case )
lowercase = model(snake_case , snake_case )
lowercase = scheduler.step(snake_case , snake_case , snake_case , generator=snake_case )
lowercase = output.prev_sample
lowercase = torch.sum(torch.abs(snake_case ) )
lowercase = torch.mean(torch.abs(snake_case ) )
assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1E-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1E-3
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if indegree[i] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
while queue:
lowercase = queue.pop(0 )
cnt += 1
topo.append(__SCREAMING_SNAKE_CASE )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
if cnt != len(__SCREAMING_SNAKE_CASE ):
print('Cycle exists' )
else:
print(__SCREAMING_SNAKE_CASE )
# Adjacency List of Graph
UpperCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 84 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
UpperCAmelCase = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
UpperCAmelCase = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
UpperCAmelCase = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
] , )
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value('float' ) ),
"references": datasets.Sequence(datasets.Value('float' ) ),
}
else:
return {
"predictions": datasets.Value('float' ),
"references": datasets.Value('float' ),
}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=None , snake_case="uniform_average" , snake_case=True ):
lowercase = mean_squared_error(
snake_case , snake_case , sample_weight=snake_case , multioutput=snake_case , squared=snake_case )
return {"mse": mse}
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
lowercase = set()
# Replace all the whitespace in our sentence
lowercase = input_str.replace(' ' , '' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(__SCREAMING_SNAKE_CASE ) == 26
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
lowercase = [False] * 26
for char in input_str:
if char.islower():
lowercase = True
elif char.isupper():
lowercase = True
return all(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = "The quick brown fox jumps over the lazy dog" , ):
return len({char for char in input_str.lower() if char.isalpha()} ) == 26
def UpperCAmelCase_ ( ):
from timeit import timeit
lowercase = 'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
print(timeit('is_pangram()' , setup=__SCREAMING_SNAKE_CASE ) )
print(timeit('is_pangram_faster()' , setup=__SCREAMING_SNAKE_CASE ) )
print(timeit('is_pangram_fastest()' , setup=__SCREAMING_SNAKE_CASE ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
raise ValueError(
'Warning: upper bound of deterministic test is exceeded. '
'Pass allow_probable=True to allow probabilistic test. '
'A return value of True indicates a probable prime.' )
# array bounds provided by analysis
lowercase = [
2047,
137_3653,
2532_6001,
32_1503_1751,
2_1523_0289_8747,
3_4747_4966_0383,
341_5500_7172_8321,
1,
382_5123_0565_4641_3051,
1,
1,
3186_6585_7834_0311_5116_7461,
3_3170_4406_4679_8873_8596_1981,
]
lowercase = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(__SCREAMING_SNAKE_CASE , 1 ):
if n < _p:
# then we have our last prime to check
lowercase = primes[:idx]
break
lowercase , lowercase = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
lowercase = False
for r in range(__SCREAMING_SNAKE_CASE ):
lowercase = pow(__SCREAMING_SNAKE_CASE , d * 2**r , __SCREAMING_SNAKE_CASE )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
lowercase = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def UpperCAmelCase_ ( ):
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(83_8201 )
assert miller_rabin(83_8207 )
# 1_373_653
assert not miller_rabin(1731_6001 )
assert miller_rabin(1731_6017 )
# 25_326_001
assert not miller_rabin(30_7838_6641 )
assert miller_rabin(30_7838_6653 )
# 3_215_031_751
assert not miller_rabin(1_7130_4557_4801 )
assert miller_rabin(1_7130_4557_4819 )
# 2_152_302_898_747
assert not miller_rabin(2_7797_9972_8307 )
assert miller_rabin(2_7797_9972_8327 )
# 3_474_749_660_383
assert not miller_rabin(113_8500_2390_9441 )
assert miller_rabin(113_8500_2390_9527 )
# 341_550_071_728_321
assert not miller_rabin(127_5041_0188_4880_4351 )
assert miller_rabin(127_5041_0188_4880_4391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(796_6646_4458_5077_8779_1867 )
assert miller_rabin(796_6646_4458_5077_8779_1951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(5528_4067_7446_6478_9766_0333 )
assert miller_rabin(5528_4067_7446_6478_9766_0359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 84 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 | 1 |
import torch
from diffusers import DiffusionPipeline
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ):
super().__init__()
self.register_modules(unet=snake_case , scheduler=snake_case )
def __call__( self ):
lowercase = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
lowercase = 1
lowercase = self.unet(snake_case , snake_case ).sample
lowercase = self.scheduler.step(snake_case , snake_case , snake_case ).prev_sample
lowercase = scheduler_output - scheduler_output + torch.ones_like(snake_case )
return result
| 84 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer 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 ):
'''simple docstring'''
_UpperCamelCase : List[str] = LongformerTokenizer
_UpperCamelCase : Any = True
_UpperCamelCase : Optional[Any] = LongformerTokenizerFast
_UpperCamelCase : Union[str, Any] = True
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
lowercase = {'unk_token': '<unk>'}
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , **snake_case ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , **snake_case ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = 'lower newer'
lowercase = 'lower newer'
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase = 'lower newer'
lowercase = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
lowercase = tokenizer.tokenize(snake_case ) # , add_prefix_space=True)
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + [tokenizer.unk_token]
lowercase = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=snake_case ) , [0, 3_1414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=snake_case ) , [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] , )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
lowercase = tokenizer.encode('sequence builders' , add_special_tokens=snake_case )
lowercase = tokenizer.encode('multi-sequence build' , add_special_tokens=snake_case )
lowercase = tokenizer.encode(
'sequence builders' , add_special_tokens=snake_case , add_prefix_space=snake_case )
lowercase = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=snake_case , add_prefix_space=snake_case )
lowercase = tokenizer.build_inputs_with_special_tokens(snake_case )
lowercase = 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 SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.get_tokenizer()
lowercase = 'Encode this sequence.'
lowercase = tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
lowercase = tokenizer.encode(snake_case , add_special_tokens=snake_case , add_prefix_space=snake_case )
lowercase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(snake_case , snake_case )
lowercase = tokenizer.encode(snake_case , add_special_tokens=snake_case , add_prefix_space=snake_case )
lowercase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(snake_case , snake_case )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
lowercase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
lowercase = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(snake_case , snake_case )
# Testing spaces after special tokens
lowercase = '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case )} ) # mask token has a left space
lowercase = tokenizer.convert_tokens_to_ids(snake_case )
lowercase = 'Encode <mask> sequence'
lowercase = 'Encode <mask>sequence'
lowercase = tokenizer.encode(snake_case )
lowercase = encoded.index(snake_case )
lowercase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(snake_case , snake_case )
lowercase = tokenizer.encode(snake_case )
lowercase = encoded.index(snake_case )
lowercase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
lowercase = self.tokenizer_class.from_pretrained(snake_case , **snake_case )
lowercase = 'A, <mask> AllenNLP sentence.'
lowercase = tokenizer_r.encode_plus(snake_case , add_special_tokens=snake_case , return_token_type_ids=snake_case )
lowercase = 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'] ) , )
lowercase = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
lowercase = 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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 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 SCREAMING_SNAKE_CASE__ ( self ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
lowercase = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
lowercase = 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 SCREAMING_SNAKE_CASE__ ( self ):
# 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})''' ):
lowercase = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
lowercase = F'''{text_of_1_token} {text_of_1_token}'''
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = 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)),
# )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
lowercase = self.rust_tokenizer_class.from_pretrained(
snake_case , use_fast=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case )
lowercase = 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 )) , )
| 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
UpperCAmelCase = logging.get_logger(__name__)
# General docstring
UpperCAmelCase = '''RegNetConfig'''
# Base docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = [1, 1088, 7, 7]
# Image classification docstring
UpperCAmelCase = '''facebook/regnet-y-040'''
UpperCAmelCase = '''tabby, tabby cat'''
UpperCAmelCase = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 3 , snake_case = 1 , snake_case = 1 , snake_case = "relu" , ):
super().__init__()
lowercase = nn.Convad(
snake_case , snake_case , kernel_size=snake_case , stride=snake_case , padding=kernel_size // 2 , groups=snake_case , bias=snake_case , )
lowercase = nn.BatchNormad(snake_case )
lowercase = ACTaFN[activation] if activation is not None else nn.Identity()
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.convolution(snake_case )
lowercase = self.normalization(snake_case )
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case ):
super().__init__()
lowercase = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowercase = config.num_channels
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
lowercase = self.embedder(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 2 ):
super().__init__()
lowercase = nn.Convad(snake_case , snake_case , kernel_size=1 , stride=snake_case , bias=snake_case )
lowercase = nn.BatchNormad(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.convolution(snake_case )
lowercase = self.normalization(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ):
super().__init__()
lowercase = nn.AdaptiveAvgPoolad((1, 1) )
lowercase = nn.Sequential(
nn.Convad(snake_case , snake_case , kernel_size=1 ) , nn.ReLU() , nn.Convad(snake_case , snake_case , kernel_size=1 ) , nn.Sigmoid() , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# b c h w -> b c 1 1
lowercase = self.pooler(snake_case )
lowercase = self.attention(snake_case )
lowercase = hidden_state * attention
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 1 ):
super().__init__()
lowercase = in_channels != out_channels or stride != 1
lowercase = max(1 , out_channels // config.groups_width )
lowercase = (
RegNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase = nn.Sequential(
RegNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(snake_case , snake_case , stride=snake_case , groups=snake_case , activation=config.hidden_act ) , RegNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=snake_case ) , )
lowercase = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = hidden_state
lowercase = self.layer(snake_case )
lowercase = self.shortcut(snake_case )
hidden_state += residual
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 1 ):
super().__init__()
lowercase = in_channels != out_channels or stride != 1
lowercase = max(1 , out_channels // config.groups_width )
lowercase = (
RegNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
lowercase = nn.Sequential(
RegNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(snake_case , snake_case , stride=snake_case , groups=snake_case , activation=config.hidden_act ) , RegNetSELayer(snake_case , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=snake_case ) , )
lowercase = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = hidden_state
lowercase = self.layer(snake_case )
lowercase = self.shortcut(snake_case )
hidden_state += residual
lowercase = self.activation(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 2 , snake_case = 2 , ):
super().__init__()
lowercase = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer
lowercase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
snake_case , snake_case , snake_case , stride=snake_case , ) , *[layer(snake_case , snake_case , snake_case ) for _ in range(depth - 1 )] , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.layers(snake_case )
return hidden_state
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case ):
super().__init__()
lowercase = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
snake_case , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowercase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(snake_case , config.depths[1:] ):
self.stages.append(RegNetStage(snake_case , snake_case , snake_case , depth=snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = False , snake_case = True ):
lowercase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowercase = hidden_states + (hidden_state,)
lowercase = stage_module(snake_case )
if output_hidden_states:
lowercase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=snake_case , hidden_states=snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : str = RegNetConfig
_UpperCamelCase : Optional[Any] = """regnet"""
_UpperCamelCase : Tuple = """pixel_values"""
_UpperCamelCase : str = True
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if isinstance(snake_case , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(snake_case , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=False ):
if isinstance(snake_case , snake_case ):
lowercase = value
UpperCAmelCase = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
UpperCAmelCase = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , __lowerCamelCase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case ):
super().__init__(snake_case )
lowercase = config
lowercase = RegNetEmbeddings(snake_case )
lowercase = RegNetEncoder(snake_case )
lowercase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = None ):
lowercase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.embedder(snake_case )
lowercase = self.encoder(
snake_case , output_hidden_states=snake_case , return_dict=snake_case )
lowercase = encoder_outputs[0]
lowercase = self.pooler(snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case , pooler_output=snake_case , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , __lowerCamelCase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case ):
super().__init__(snake_case )
lowercase = config.num_labels
lowercase = RegNetModel(snake_case )
# classification head
lowercase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , ):
lowercase = return_dict if return_dict is not None else self.config.use_return_dict
lowercase = self.regnet(snake_case , output_hidden_states=snake_case , return_dict=snake_case )
lowercase = outputs.pooler_output if return_dict else outputs[1]
lowercase = self.classifier(snake_case )
lowercase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowercase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowercase = 'single_label_classification'
else:
lowercase = 'multi_label_classification'
if self.config.problem_type == "regression":
lowercase = MSELoss()
if self.num_labels == 1:
lowercase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowercase = loss_fct(snake_case , snake_case )
elif self.config.problem_type == "single_label_classification":
lowercase = CrossEntropyLoss()
lowercase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowercase = BCEWithLogitsLoss()
lowercase = loss_fct(snake_case , snake_case )
if not return_dict:
lowercase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=snake_case , logits=snake_case , hidden_states=outputs.hidden_states )
| 84 |
import unittest
from transformers import 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 (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTDoubleHeadsModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = self.num_labels
lowercase = OpenAIGPTForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCamelCase : str = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=False ):
lowercase = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=snake_case , )
lowercase = inputs_dict['labels']
lowercase = inputs_dict['labels']
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=snake_case , )
lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = OpenAIGPTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(snake_case )
lowercase = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=snake_case ) # the president is
lowercase = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 84 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase = logging.get_logger(__name__)
@dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **snake_case ):
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase = deprecated_arg[3:]
lowercase = not kwargs.pop(snake_case )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
lowercase = kwargs.pop('tpu_name' , self.tpu_name )
lowercase = kwargs.pop('device_idx' , self.device_idx )
lowercase = kwargs.pop('eager_mode' , self.eager_mode )
lowercase = kwargs.pop('use_xla' , self.use_xla )
super().__init__(**snake_case )
_UpperCamelCase : str = field(
default=__lowerCamelCase , metadata={"""help""": """Name of TPU"""} , )
_UpperCamelCase : int = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
_UpperCamelCase : bool = field(default=__lowerCamelCase , metadata={"""help""": """Benchmark models in eager model."""} )
_UpperCamelCase : bool = field(
default=__lowerCamelCase , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
lowercase = None
if self.tpu:
try:
if self.tpu_name:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
lowercase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
lowercase = None
return tpu
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
lowercase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , 'GPU' )
lowercase = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , 'GPU' ) # disable GPU
lowercase = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
return self._setup_tpu is not None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
return self._setup_strategy
@property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
return tf.config.list_physical_devices('GPU' )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
requires_backends(self , ['tf'] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.n_gpu > 0
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
from maths.prime_check import is_prime
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__SCREAMING_SNAKE_CASE )
if is_prime(__SCREAMING_SNAKE_CASE ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
lowercase = False
lowercase = index + i
lowercase = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 9999_6666_3333 ):
lowercase = math.floor(math.sqrt(__SCREAMING_SNAKE_CASE ) ) + 100
lowercase = prime_sieve(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = primes[prime_index]
while (last_prime**2) <= limit:
lowercase = primes[prime_index + 1]
lowercase = last_prime**2
lowercase = next_prime**2
# Get numbers divisible by lps(current)
lowercase = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowercase = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowercase = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowercase = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 84 | 1 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 84 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
UpperCAmelCase = re.compile(R'''^\s*try:''')
# Catches a line with else:
UpperCAmelCase = re.compile(R'''^\s*else:''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if _re_test_backend.search(__SCREAMING_SNAKE_CASE ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ):
lowercase = _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ).groups()[0]
lowercase = re.findall(r'\[([^\]]+)\]' , __SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_between_brackets.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
def find_duplicates(__SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def UpperCAmelCase_ ( ):
lowercase = []
for root, _, files in os.walk(__SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' )
lowercase = parse_init(__SCREAMING_SNAKE_CASE )
if objects is not None:
lowercase = analyze_results(*__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = []
for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / folder).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace(os.path.sep , '.' )
submodules.append(__SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / fname).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__SCREAMING_SNAKE_CASE )
return submodules
UpperCAmelCase = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def UpperCAmelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
lowercase = direct_transformers_import(__SCREAMING_SNAKE_CASE )
lowercase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
lowercase = f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , __SCREAMING_SNAKE_CASE ) ) )
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 84 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Any = LDMTextToImagePipeline
_UpperCamelCase : Any = TEXT_TO_IMAGE_PARAMS - {
"""negative_prompt""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
"""prompt_embeds""",
}
_UpperCamelCase : List[str] = PipelineTesterMixin.required_optional_params - {
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
_UpperCamelCase : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCamelCase : str = False
def SCREAMING_SNAKE_CASE__ ( self ):
torch.manual_seed(0 )
lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
lowercase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
lowercase = AutoencoderKL(
block_out_channels=(32, 64) , in_channels=3 , out_channels=3 , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , latent_channels=4 , )
torch.manual_seed(0 )
lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
lowercase = CLIPTextModel(snake_case )
lowercase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowercase = {
'unet': unet,
'scheduler': scheduler,
'vqvae': vae,
'bert': text_encoder,
'tokenizer': tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=0 ):
if str(snake_case ).startswith('mps' ):
lowercase = torch.manual_seed(snake_case )
else:
lowercase = torch.Generator(device=snake_case ).manual_seed(snake_case )
lowercase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase = self.get_dummy_components()
lowercase = LDMTextToImagePipeline(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_dummy_inputs(snake_case )
lowercase = pipe(**snake_case ).images
lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
lowercase = np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=torch.floataa , snake_case=0 ):
lowercase = torch.manual_seed(snake_case )
lowercase = np.random.RandomState(snake_case ).standard_normal((1, 4, 32, 32) )
lowercase = torch.from_numpy(snake_case ).to(device=snake_case , dtype=snake_case )
lowercase = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_inputs(snake_case )
lowercase = pipe(**snake_case ).images
lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
lowercase = np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878] )
lowercase = np.abs(expected_slice - image_slice ).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=torch.floataa , snake_case=0 ):
lowercase = torch.manual_seed(snake_case )
lowercase = np.random.RandomState(snake_case ).standard_normal((1, 4, 32, 32) )
lowercase = torch.from_numpy(snake_case ).to(device=snake_case , dtype=snake_case )
lowercase = {
'prompt': 'A painting of a squirrel eating a burger',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256' ).to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
lowercase = self.get_inputs(snake_case )
lowercase = pipe(**snake_case ).images[0]
lowercase = load_numpy(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy' )
lowercase = np.abs(expected_image - image ).max()
assert max_diff < 1E-3
| 84 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = None
def __str__( self ):
return F'''{self.data}'''
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
lowercase = None
def __iter__( self ):
lowercase = self.top
while node:
yield node.data
lowercase = node.next
def __str__( self ):
return "->".join([str(snake_case ) for item in self] )
def __len__( self ):
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.top is None
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = Node(snake_case )
if not self.is_empty():
lowercase = self.top
lowercase = node
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case )
lowercase = self.top
lowercase = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 | 1 |
UpperCAmelCase = '''
# 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
'''
UpperCAmelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 84 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=7 , snake_case=3 , snake_case=18 , snake_case=30 , snake_case=400 , snake_case=True , snake_case=None , snake_case=True , ):
lowercase = size if size is not None else {'height': 18, 'width': 18}
lowercase = parent
lowercase = batch_size
lowercase = num_channels
lowercase = image_size
lowercase = min_resolution
lowercase = max_resolution
lowercase = do_resize
lowercase = size
lowercase = apply_ocr
def SCREAMING_SNAKE_CASE__ ( self ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Any = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LayoutLMvaImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case , 'do_resize' ) )
self.assertTrue(hasattr(snake_case , 'size' ) )
self.assertTrue(hasattr(snake_case , 'apply_ocr' ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
lowercase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
# Initialize image_processing
lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
lowercase = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , snake_case )
self.assertIsInstance(encoding.boxes , snake_case )
# Test batched
lowercase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self ):
# Initialize image_processing
lowercase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , np.ndarray )
# Test not batched input
lowercase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
lowercase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self ):
# Initialize image_processing
lowercase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , torch.Tensor )
# Test not batched input
lowercase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
lowercase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
def SCREAMING_SNAKE_CASE__ ( self ):
# with apply_OCR = True
lowercase = LayoutLMvaImageProcessor()
from datasets import load_dataset
lowercase = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
lowercase = Image.open(ds[0]['file'] ).convert('RGB' )
lowercase = image_processing(snake_case , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
lowercase = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
lowercase = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , snake_case )
self.assertListEqual(encoding.boxes , snake_case )
# with apply_OCR = False
lowercase = LayoutLMvaImageProcessor(apply_ocr=snake_case )
lowercase = image_processing(snake_case , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 84 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : Dict = """dummy_data"""
_UpperCamelCase : Optional[int] = """datasets"""
_UpperCamelCase : Tuple = False
def __init__( self , snake_case , snake_case , snake_case , snake_case = None , snake_case = False , snake_case = True , snake_case = None , ):
lowercase = 0
lowercase = dataset_name
lowercase = cache_dir
lowercase = use_local_dummy_data
lowercase = config
# download_callbacks take a single url as input
lowercase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase = str(snake_case )
# to be downloaded
lowercase = None
lowercase = None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._dummy_file is None:
lowercase = self.download_dummy_data()
return self._dummy_file
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase = cached_path(
snake_case , cache_dir=self.cache_dir , extract_compressed_file=snake_case , force_extract=snake_case )
return os.path.join(snake_case , self.dummy_file_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._bucket_url is None:
lowercase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def SCREAMING_SNAKE_CASE__ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case , snake_case ):
return self.create_dummy_data_dict(snake_case , snake_case )
elif isinstance(snake_case , (list, tuple) ):
return self.create_dummy_data_list(snake_case , snake_case )
else:
return self.create_dummy_data_single(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case , **snake_case ):
return path
def SCREAMING_SNAKE_CASE__ ( self ):
return {}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case , snake_case ):
for single_url in single_urls:
download_callback(snake_case )
else:
lowercase = single_urls
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(snake_case , snake_case ):
lowercase = [os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) ) for x in single_urls]
else:
lowercase = single_urls
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) )
lowercase = value
# make sure that values are unique
if all(isinstance(snake_case , snake_case ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , snake_case ) ) for url in data_url )
lowercase = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase = [data_url[0]] * len(snake_case )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(snake_case )
return dummy_data_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(snake_case ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
def _iter_archive_members(snake_case ):
# this preserves the order of the members inside the ZIP archive
lowercase = Path(self.dummy_file ).parent
lowercase = path.relative_to(snake_case )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case )
lowercase = Path(snake_case )
lowercase = _iter_archive_members(snake_case ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(snake_case ).as_posix(), file_path.open('rb' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
lowercase = [paths]
for path in paths:
if os.path.isfile(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(snake_case ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(snake_case , snake_case )
| 84 | 1 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
UpperCAmelCase = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase = XLMProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = XLMProphetNetForConditionalGeneration.from_pretrained(
__SCREAMING_SNAKE_CASE , output_loading_info=__SCREAMING_SNAKE_CASE )
else:
lowercase = ProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = ProphetNetForConditionalGeneration.from_pretrained(
__SCREAMING_SNAKE_CASE , output_loading_info=__SCREAMING_SNAKE_CASE )
lowercase = ['key_proj', 'value_proj', 'query_proj']
lowercase = {
'self_attn': 'ngram_self_attn',
'cross_attn': 'encoder_attn',
'cross_attn_layer_norm': 'encoder_attn_layer_norm',
'feed_forward_layer_norm': 'final_layer_norm',
'feed_forward': '',
'intermediate': 'fc1',
'output': 'fc2',
'key_proj': 'k_proj',
'query_proj': 'q_proj',
'value_proj': 'v_proj',
'word_embeddings': 'embed_tokens',
'embeddings_layer_norm': 'emb_layer_norm',
'relative_pos_embeddings': 'relative_linear',
'ngram_embeddings': 'ngram_input_embed',
'position_embeddings': 'embed_positions',
}
for key in loading_info["missing_keys"]:
lowercase = key.split('.' )
if attributes[0] == "lm_head":
lowercase = prophet
lowercase = prophet_old
else:
lowercase = prophet.prophetnet
lowercase = prophet_old.model
lowercase = False
for attribute in attributes:
if attribute in mapping:
lowercase = mapping[attribute]
if not hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = attribute
elif hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase = old_model.weight
logger.info(F'''{attribute} is initialized.''' )
lowercase = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase = old_model.bias
logger.info(F'''{attribute} is initialized''' )
lowercase = True
break
elif attribute in special_keys and hasattr(__SCREAMING_SNAKE_CASE , 'in_proj_weight' ):
lowercase = old_model.in_proj_weight.shape[0] // 3
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase = True
break
if attribute.isdigit():
lowercase = model[int(__SCREAMING_SNAKE_CASE )]
lowercase = old_model[int(__SCREAMING_SNAKE_CASE )]
else:
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if old_attribute == "":
lowercase = old_model
else:
if not hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise ValueError(F'''{old_model} does not have {old_attribute}''' )
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if not is_key_init:
raise ValueError(F'''{key} was not correctly initialized!''' )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCAmelCase = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 84 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('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 SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 , __SCREAMING_SNAKE_CASE = 10 ):
lowercase = defaultdict(__SCREAMING_SNAKE_CASE )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
lowercase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
lowercase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__SCREAMING_SNAKE_CASE , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 84 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase = abspath(join(dirname(__file__), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
from transformers.testing_utils import pytest_terminal_summary_main
lowercase = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(__SCREAMING_SNAKE_CASE , id=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowercase = 0
# Doctest custom flag to ignore output.
UpperCAmelCase = doctest.register_optionflag('''IGNORE_RESULT''')
UpperCAmelCase = doctest.OutputChecker
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , snake_case , snake_case , snake_case )
UpperCAmelCase = CustomOutputChecker
UpperCAmelCase = HfDoctestModule
UpperCAmelCase = HfDocTestParser
| 84 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(snake_case ) , torch_builtin(snake_case ) ) )
self.assertFalse(torch.allclose(gelu_python(snake_case ) , gelu_new(snake_case ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase = get_activation('gelu' )
lowercase = get_activation('gelu_10' )
lowercase = torch_builtin(snake_case )
lowercase = geluaa(snake_case )
lowercase = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(snake_case ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def SCREAMING_SNAKE_CASE__ ( self ):
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(snake_case ):
get_activation('bogus' )
with self.assertRaises(snake_case ):
get_activation(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = get_activation('gelu' )
lowercase = 1
lowercase = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(snake_case ):
lowercase = acta.a
| 84 |
import torch
from torch import nn
class A_ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False ):
super().__init__()
lowercase = n_token
lowercase = d_embed
lowercase = d_proj
lowercase = cutoffs + [n_token]
lowercase = [0] + self.cutoffs
lowercase = div_val
lowercase = self.cutoffs[0]
lowercase = len(self.cutoffs ) - 1
lowercase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
lowercase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
lowercase = nn.Parameter(torch.zeros(self.n_clusters ) )
lowercase = nn.ModuleList()
lowercase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
else:
self.out_projs.append(snake_case )
self.out_layers.append(nn.Linear(snake_case , snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case , snake_case ) ) )
self.out_layers.append(nn.Linear(snake_case , r_idx - l_idx ) )
lowercase = keep_order
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
if proj is None:
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
lowercase = nn.functional.linear(snake_case , proj.t().contiguous() )
lowercase = nn.functional.linear(snake_case , snake_case , bias=snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None , snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
lowercase = hidden[..., :-1, :].contiguous()
lowercase = labels[..., 1:].contiguous()
lowercase = hidden.view(-1 , hidden.size(-1 ) )
lowercase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
lowercase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
lowercase = labels != -100
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = (
-nn.functional.log_softmax(snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
lowercase = nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
if labels is None:
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
lowercase = torch.zeros_like(snake_case , dtype=hidden.dtype , device=hidden.device )
lowercase = 0
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
lowercase = (labels >= l_idx) & (labels < r_idx)
lowercase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
lowercase = labels.index_select(0 , snake_case ) - l_idx
lowercase = head_logprob.index_select(0 , snake_case )
lowercase = hidden.index_select(0 , snake_case )
else:
lowercase = hidden
if i == 0:
if labels is not None:
lowercase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
lowercase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
lowercase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
lowercase = logprob_i
if labels is not None:
if (hasattr(self , 'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 , snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if self.n_clusters == 0:
lowercase = self._compute_logit(snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(snake_case , dim=-1 )
else:
# construct weights and biases
lowercase , lowercase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
lowercase , lowercase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowercase = self.out_layers[0].weight[l_idx:r_idx]
lowercase = self.out_layers[0].bias[l_idx:r_idx]
else:
lowercase = self.out_layers[i].weight
lowercase = self.out_layers[i].bias
if i == 0:
lowercase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
lowercase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(snake_case )
biases.append(snake_case )
lowercase , lowercase , lowercase = weights[0], biases[0], self.out_projs[0]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = [0] + self.cutoffs
for i in range(len(snake_case ) - 1 ):
lowercase , lowercase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
lowercase = head_logprob[:, : self.cutoffs[0]]
else:
lowercase , lowercase , lowercase = weights[i], biases[i], self.out_projs[i]
lowercase = self._compute_logit(snake_case , snake_case , snake_case , snake_case )
lowercase = nn.functional.log_softmax(snake_case , dim=1 )
lowercase = head_logprob[:, -i] + tail_logprob_i
lowercase = logprob_i
return out
| 84 | 1 |
import string
import numpy
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return b if a == 0 else greatest_common_divisor(b % a , __SCREAMING_SNAKE_CASE )
class A_ :
'''simple docstring'''
_UpperCamelCase : Tuple = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
_UpperCamelCase : List[str] = numpy.vectorize(lambda __lowerCamelCase : x % 36 )
_UpperCamelCase : str = numpy.vectorize(__lowerCamelCase )
def __init__( self , snake_case ):
lowercase = self.modulus(snake_case ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
lowercase = encrypt_key.shape[0]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.key_string.index(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.key_string[round(snake_case )]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
lowercase = det % len(self.key_string )
lowercase = len(self.key_string )
if greatest_common_divisor(snake_case , len(self.key_string ) ) != 1:
lowercase = (
F'''determinant modular {req_l} of encryption key({det}) '''
F'''is not co prime w.r.t {req_l}.\nTry another key.'''
)
raise ValueError(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = [char for char in text.upper() if char in self.key_string]
lowercase = chars[-1]
while len(snake_case ) % self.break_key != 0:
chars.append(snake_case )
return "".join(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.process_text(text.upper() )
lowercase = ''
for i in range(0 , len(snake_case ) - self.break_key + 1 , self.break_key ):
lowercase = text[i : i + self.break_key]
lowercase = [self.replace_letters(snake_case ) for char in batch]
lowercase = numpy.array([vec] ).T
lowercase = self.modulus(self.encrypt_key.dot(snake_case ) ).T.tolist()[
0
]
lowercase = ''.join(
self.replace_digits(snake_case ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
lowercase = det % len(self.key_string )
lowercase = None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
lowercase = i
break
lowercase = (
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.make_decrypt_key()
lowercase = self.process_text(text.upper() )
lowercase = ''
for i in range(0 , len(snake_case ) - self.break_key + 1 , self.break_key ):
lowercase = text[i : i + self.break_key]
lowercase = [self.replace_letters(snake_case ) for char in batch]
lowercase = numpy.array([vec] ).T
lowercase = self.modulus(decrypt_key.dot(snake_case ) ).T.tolist()[0]
lowercase = ''.join(
self.replace_digits(snake_case ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def UpperCAmelCase_ ( ):
lowercase = int(input('Enter the order of the encryption key: ' ) )
lowercase = []
print('Enter each row of the encryption key with space separated integers' )
for _ in range(__SCREAMING_SNAKE_CASE ):
lowercase = [int(__SCREAMING_SNAKE_CASE ) for x in input().split()]
hill_matrix.append(__SCREAMING_SNAKE_CASE )
lowercase = HillCipher(numpy.array(__SCREAMING_SNAKE_CASE ) )
print('Would you like to encrypt or decrypt some text? (1 or 2)' )
lowercase = input('\n1. Encrypt\n2. Decrypt\n' )
if option == "1":
lowercase = input('What text would you like to encrypt?: ' )
print('Your encrypted text is:' )
print(hc.encrypt(__SCREAMING_SNAKE_CASE ) )
elif option == "2":
lowercase = input('What text would you like to decrypt?: ' )
print('Your decrypted text is:' )
print(hc.decrypt(__SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 84 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
'least one and the same number of values, each of which must be of type '
'int or float.' )
if len(snake_case ) != 0:
lowercase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(snake_case ) != cols:
raise error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise error
lowercase = rows
else:
lowercase = []
def SCREAMING_SNAKE_CASE__ ( self ):
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.rows[0] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return (self.num_rows, self.num_columns)
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.order[0] == self.order[1]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return bool(self.determinant() )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(snake_case ).determinant()
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
if (row + column) % 2 == 0:
return self.get_minor(snake_case , snake_case )
return -1 * self.get_minor(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[self.get_minor(snake_case , snake_case ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.determinant()
if not determinant:
raise TypeError('Only matrices with a non-zero determinant have an inverse' )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
return str(self.rows )
def __str__( self ):
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
'[' + '. '.join([str(snake_case ) for value in row] ) + '.]'
for row in self.rows
] )
+ "]"
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError('Row must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in row:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_columns:
raise ValueError(
'Row must be equal in length to the other rows in the matrix' )
if position is None:
self.rows.append(snake_case )
else:
lowercase = self.rows[0:position] + [row] + self.rows[position:]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = TypeError(
'Column must be a list containing all ints and/or floats' )
if not isinstance(snake_case , snake_case ):
raise type_error
for value in column:
if not isinstance(snake_case , (int, float) ):
raise type_error
if len(snake_case ) != self.num_rows:
raise ValueError(
'Column must be equal in length to the other columns in the matrix' )
if position is None:
lowercase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowercase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , snake_case ):
return not self == other
def __neg__( self ):
return self * -1
def __add__( self , snake_case ):
if self.order != other.order:
raise ValueError('Addition requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , snake_case ):
if self.order != other.order:
raise ValueError('Subtraction requires matrices of the same order' )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , snake_case ):
if isinstance(snake_case , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(snake_case , snake_case ):
if self.num_columns != other.num_rows:
raise ValueError(
'The number of columns in the first matrix must '
'be equal to the number of rows in the second' )
return Matrix(
[
[Matrix.dot_product(snake_case , snake_case ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
'A Matrix can only be multiplied by an int, float, or another matrix' )
def __pow__( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise TypeError('A Matrix can only be raised to the power of an int' )
if not self.is_square:
raise ValueError('Only square matrices can be raised to a power' )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
'Only invertable matrices can be raised to a negative power' )
lowercase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case ):
return sum(row[i] * column[i] for i in range(len(snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE , params=__SCREAMING_SNAKE_CASE ).content , 'html.parser' )
lowercase = soup.find('div' , attrs={'class': 'gs_ri'} )
lowercase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase = {
'''title''': (
'''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
'''capacitance, volumetric capacitance, and energy density'''
),
'''journal''': '''Chem. Mater.''',
'''volume''': 30,
'''pages''': '''3979-3990''',
'''year''': 2018,
'''hl''': '''en''',
}
print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))
| 84 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=8 ):
lowercase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , ):
super().__init__()
self.register_modules(
unet=snake_case , scheduler=snake_case , movq=snake_case , )
lowercase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
if latents is None:
lowercase = randn_tensor(snake_case , generator=snake_case , device=snake_case , dtype=snake_case )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase = latents.to(snake_case )
lowercase = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
lowercase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=0 ):
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
lowercase = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=snake_case )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase , lowercase = cpu_offload_with_hook(snake_case , snake_case , prev_module_hook=snake_case )
# We'll offload the last model manually.
lowercase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE__ ( self ):
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(snake_case , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(snake_case )
def __call__( self , snake_case , snake_case , snake_case = 512 , snake_case = 512 , snake_case = 100 , snake_case = 4.0 , snake_case = 1 , snake_case = None , snake_case = None , snake_case = "pil" , snake_case = True , ):
lowercase = self._execution_device
lowercase = guidance_scale > 1.0
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
lowercase = image_embeds.shape[0] * num_images_per_prompt
if isinstance(snake_case , snake_case ):
lowercase = torch.cat(snake_case , dim=0 )
if do_classifier_free_guidance:
lowercase = image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = negative_image_embeds.repeat_interleave(snake_case , dim=0 )
lowercase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=snake_case )
self.scheduler.set_timesteps(snake_case , device=snake_case )
lowercase = self.scheduler.timesteps
lowercase = self.unet.config.in_channels
lowercase , lowercase = downscale_height_and_width(snake_case , snake_case , self.movq_scale_factor )
# create initial latent
lowercase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , snake_case , snake_case , snake_case , self.scheduler , )
for i, t in enumerate(self.progress_bar(snake_case ) ):
# expand the latents if we are doing classifier free guidance
lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase = {'image_embeds': image_embeds}
lowercase = self.unet(
sample=snake_case , timestep=snake_case , encoder_hidden_states=snake_case , added_cond_kwargs=snake_case , return_dict=snake_case , )[0]
if do_classifier_free_guidance:
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
lowercase , lowercase = noise_pred.chunk(2 )
lowercase , lowercase = variance_pred.chunk(2 )
lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase , lowercase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase = self.scheduler.step(
snake_case , snake_case , snake_case , generator=snake_case , )[0]
# post-processing
lowercase = self.movq.decode(snake_case , force_not_quantize=snake_case )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase = image * 0.5 + 0.5
lowercase = image.clamp(0 , 1 )
lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case )
| 84 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for attribute in key.split('.' ):
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if weight_type is not None:
lowercase = getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ).shape
else:
lowercase = 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":
lowercase = value
elif weight_type == "weight_g":
lowercase = value
elif weight_type == "weight_v":
lowercase = value
elif weight_type == "bias":
lowercase = value
else:
lowercase = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
lowercase = fairseq_model.state_dict()
lowercase = hf_model.feature_extractor
lowercase = hf_model.adapter
for name, value in fairseq_dict.items():
lowercase = False
if "conv_layers" in name:
load_conv_layer(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == 'group' , )
lowercase = True
elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.'] ):
load_adapter(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
lowercase = True
if "*" in mapped_key:
lowercase = name.split(__SCREAMING_SNAKE_CASE )[0].split('.' )[-2]
lowercase = mapped_key.replace('*' , __SCREAMING_SNAKE_CASE )
if "weight_g" in name:
lowercase = 'weight_g'
elif "weight_v" in name:
lowercase = 'weight_v'
elif "bias" in name:
lowercase = 'bias'
elif "weight" in name:
lowercase = 'weight'
else:
lowercase = None
set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(__SCREAMING_SNAKE_CASE )
logger.warning(F'''Unused weights: {unused_weights}''' )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = full_name.split('conv_layers.' )[-1]
lowercase = name.split('.' )
lowercase = int(items[0] )
lowercase = 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.'''
)
lowercase = 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.'''
)
lowercase = 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."
)
lowercase = 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.'''
)
lowercase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = full_name.split('adaptor.' )[-1]
lowercase = name.split('.' )
if items[1].isdigit():
lowercase = int(items[1] )
else:
lowercase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
lowercase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
lowercase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
lowercase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
lowercase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
lowercase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
lowercase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase , lowercase = emb.weight.shape
lowercase = nn.Linear(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , bias=__SCREAMING_SNAKE_CASE )
lowercase = emb.weight.data
return lin_layer
@torch.no_grad()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ):
lowercase = WavaVecaConfig.from_pretrained(
__SCREAMING_SNAKE_CASE , add_adapter=__SCREAMING_SNAKE_CASE , adapter_stride=__SCREAMING_SNAKE_CASE , adapter_kernel_size=__SCREAMING_SNAKE_CASE , use_auth_token=__SCREAMING_SNAKE_CASE , output_hidden_size=__SCREAMING_SNAKE_CASE , )
lowercase = MBartConfig.from_pretrained(__SCREAMING_SNAKE_CASE )
# load model
lowercase , lowercase , lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'config_yaml': config_yaml_path,
'data': '/'.join(dict_path.split('/' )[:-1] ),
'w2v_path': checkpoint_path,
'load_pretrained_decoder_from': None,
} , )
lowercase = model[0].eval()
# load feature extractor
lowercase = WavaVecaFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE , use_auth_token=__SCREAMING_SNAKE_CASE )
# set weights for wav2vec2 encoder
lowercase = WavaVecaModel(__SCREAMING_SNAKE_CASE )
recursively_load_weights_wavaveca(model.encoder , __SCREAMING_SNAKE_CASE )
# load decoder weights
lowercase = MBartForCausalLM(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__SCREAMING_SNAKE_CASE )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
lowercase = SpeechEncoderDecoderModel(encoder=__SCREAMING_SNAKE_CASE , decoder=__SCREAMING_SNAKE_CASE )
lowercase = False
lowercase = MBartaaTokenizer(__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE )
lowercase = hf_wavavec.config.to_dict()
lowercase = tokenizer.pad_token_id
lowercase = tokenizer.bos_token_id
lowercase = tokenizer.eos_token_id
lowercase = 'mbart50'
lowercase = 'wav2vec2'
lowercase = tokenizer.eos_token_id
lowercase = 25_0004
lowercase = tokenizer.eos_token_id
lowercase = SpeechEncoderDecoderConfig.from_dict(__SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE )
feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-xls-r-1b''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/mbart-large-50-one-to-many-mmt''',
type=str,
help='''Path to hf decoder checkpoint config''',
)
parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')
UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = """sew-d"""
def __init__( self , snake_case=32 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case=2 , snake_case=512 , snake_case=256 , snake_case=True , snake_case=True , snake_case=("p2c", "c2p") , snake_case="layer_norm" , snake_case="gelu_python" , snake_case=0.1 , snake_case=0.1 , snake_case=0.1 , snake_case=0.0 , snake_case=0.1 , snake_case=0.02 , snake_case=1E-7 , snake_case=1E-5 , snake_case="group" , snake_case="gelu" , snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case=False , snake_case=128 , snake_case=16 , snake_case=True , snake_case=0.05 , snake_case=10 , snake_case=2 , snake_case=0.0 , snake_case=10 , snake_case=0 , snake_case="mean" , snake_case=False , snake_case=False , snake_case=256 , snake_case=0 , snake_case=1 , snake_case=2 , **snake_case , ):
super().__init__(**snake_case , pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case )
lowercase = hidden_size
lowercase = feat_extract_norm
lowercase = feat_extract_activation
lowercase = list(snake_case )
lowercase = list(snake_case )
lowercase = list(snake_case )
lowercase = conv_bias
lowercase = num_conv_pos_embeddings
lowercase = num_conv_pos_embedding_groups
lowercase = len(self.conv_dim )
lowercase = num_hidden_layers
lowercase = intermediate_size
lowercase = squeeze_factor
lowercase = max_position_embeddings
lowercase = position_buckets
lowercase = share_att_key
lowercase = relative_attention
lowercase = norm_rel_ebd
lowercase = list(snake_case )
lowercase = hidden_act
lowercase = num_attention_heads
lowercase = hidden_dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = feat_proj_dropout
lowercase = final_dropout
lowercase = layer_norm_eps
lowercase = feature_layer_norm_eps
lowercase = initializer_range
lowercase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase = apply_spec_augment
lowercase = mask_time_prob
lowercase = mask_time_length
lowercase = mask_time_min_masks
lowercase = mask_feature_prob
lowercase = mask_feature_length
lowercase = mask_feature_min_masks
# ctc loss
lowercase = ctc_loss_reduction
lowercase = ctc_zero_infinity
# sequence classification
lowercase = use_weighted_layer_sum
lowercase = classifier_proj_size
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 84 |
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCREAMING_SNAKE_CASE ):
if is_palindrome(__SCREAMING_SNAKE_CASE ) and is_palindrome(bin(__SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 84 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 |
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """conditional_detr"""
_UpperCamelCase : Any = ["""past_key_values"""]
_UpperCamelCase : Optional[Any] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self , snake_case=True , snake_case=None , snake_case=3 , snake_case=300 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=6 , snake_case=2048 , snake_case=8 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case="relu" , snake_case=256 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1.0 , snake_case=False , snake_case="sine" , snake_case="resnet50" , snake_case=True , snake_case=False , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=1 , snake_case=1 , snake_case=2 , snake_case=5 , snake_case=2 , snake_case=0.25 , **snake_case , ):
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
lowercase = CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(snake_case , snake_case ):
lowercase = backbone_config.get('model_type' )
lowercase = CONFIG_MAPPING[backbone_model_type]
lowercase = config_class.from_dict(snake_case )
lowercase = use_timm_backbone
lowercase = backbone_config
lowercase = num_channels
lowercase = num_queries
lowercase = d_model
lowercase = encoder_ffn_dim
lowercase = encoder_layers
lowercase = encoder_attention_heads
lowercase = decoder_ffn_dim
lowercase = decoder_layers
lowercase = decoder_attention_heads
lowercase = dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = activation_function
lowercase = init_std
lowercase = init_xavier_std
lowercase = encoder_layerdrop
lowercase = decoder_layerdrop
lowercase = encoder_layers
lowercase = auxiliary_loss
lowercase = position_embedding_type
lowercase = backbone
lowercase = use_pretrained_backbone
lowercase = dilation
# Hungarian matcher
lowercase = class_cost
lowercase = bbox_cost
lowercase = giou_cost
# Loss coefficients
lowercase = mask_loss_coefficient
lowercase = dice_loss_coefficient
lowercase = cls_loss_coefficient
lowercase = bbox_loss_coefficient
lowercase = giou_loss_coefficient
lowercase = focal_alpha
super().__init__(is_encoder_decoder=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return self.d_model
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase = self.backbone_config.to_dict()
lowercase = self.__class__.model_type
return output
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = version.parse("""1.11""" )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 1E-5
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return 12
| 84 | 1 |
from timeit import timeit
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if number < 0:
raise ValueError('the value of input must not be negative' )
lowercase = 0
while number:
number &= number - 1
result += 1
return result
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if number < 0:
raise ValueError('the value of input must not be negative' )
lowercase = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def UpperCAmelCase_ ( ):
def do_benchmark(__SCREAMING_SNAKE_CASE ) -> None:
lowercase = 'import __main__ as z'
print(F'''Benchmark when {number = }:''' )
print(F'''{get_set_bits_count_using_modulo_operator(__SCREAMING_SNAKE_CASE ) = }''' )
lowercase = timeit('z.get_set_bits_count_using_modulo_operator(25)' , setup=__SCREAMING_SNAKE_CASE )
print(F'''timeit() runs in {timing} seconds''' )
print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(__SCREAMING_SNAKE_CASE ) = }''' )
lowercase = timeit(
'z.get_set_bits_count_using_brian_kernighans_algorithm(25)' , setup=__SCREAMING_SNAKE_CASE , )
print(F'''timeit() runs in {timing} seconds''' )
for number in (25, 37, 58, 0):
do_benchmark(__SCREAMING_SNAKE_CASE )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import argparse
import datetime
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
lowercase = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(__SCREAMING_SNAKE_CASE ) < 11:
raise ValueError('Must be 10 characters long' )
# Get month
lowercase = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('Month must be between 1 - 12' )
lowercase = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get day
lowercase = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('Date must be between 1 - 31' )
# Get second separator
lowercase = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get year
lowercase = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'Year out of range. There has to be some sort of limit...right?' )
# Get datetime obj for validation
lowercase = datetime.date(int(__SCREAMING_SNAKE_CASE ) , int(__SCREAMING_SNAKE_CASE ) , int(__SCREAMING_SNAKE_CASE ) )
# Start math
if m <= 2:
lowercase = y - 1
lowercase = m + 12
# maths var
lowercase = int(str(__SCREAMING_SNAKE_CASE )[:2] )
lowercase = int(str(__SCREAMING_SNAKE_CASE )[2:] )
lowercase = int(2.6 * m - 5.39 )
lowercase = int(c / 4 )
lowercase = int(k / 4 )
lowercase = int(d + k )
lowercase = int(t + u + v + x )
lowercase = int(z - (2 * c) )
lowercase = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('The date was evaluated incorrectly. Contact developer.' )
# Response
lowercase = F'''Your date {date_input}, is a {days[str(__SCREAMING_SNAKE_CASE )]}!'''
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase = argparse.ArgumentParser(
description=(
'''Find out what day of the week nearly any date is or was. Enter '''
'''date as a string in the mm-dd-yyyy or mm/dd/yyyy format'''
)
)
parser.add_argument(
'''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)'''
)
UpperCAmelCase = parser.parse_args()
zeller(args.date_input)
| 84 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = []
lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if indegree[i] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
while queue:
lowercase = queue.pop(0 )
cnt += 1
topo.append(__SCREAMING_SNAKE_CASE )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__SCREAMING_SNAKE_CASE )
if cnt != len(__SCREAMING_SNAKE_CASE ):
print('Cycle exists' )
else:
print(__SCREAMING_SNAKE_CASE )
# Adjacency List of Graph
UpperCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 84 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCAmelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCAmelCase = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = '''cpu'''
UpperCAmelCase = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
UpperCAmelCase = '''path-to-your-trained-model'''
UpperCAmelCase = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase = pipe.to(device)
# to channels last
UpperCAmelCase = pipe.unet.to(memory_format=torch.channels_last)
UpperCAmelCase = pipe.vae.to(memory_format=torch.channels_last)
UpperCAmelCase = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
UpperCAmelCase = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
UpperCAmelCase = torch.randn(2, 4, 64, 64)
UpperCAmelCase = torch.rand(1) * 999
UpperCAmelCase = torch.randn(2, 77, 768)
UpperCAmelCase = (sample, timestep, encoder_hidden_status)
try:
UpperCAmelCase = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
UpperCAmelCase = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
UpperCAmelCase = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
UpperCAmelCase = 666
UpperCAmelCase = torch.Generator(device).manual_seed(seed)
UpperCAmelCase = {'''generator''': generator}
if args.steps is not None:
UpperCAmelCase = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
UpperCAmelCase = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 84 | 1 |
import importlib
import inspect
import os
import re
# 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
UpperCAmelCase = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase = spec.loader.load_module()
UpperCAmelCase = 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)`
UpperCAmelCase = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
UpperCAmelCase = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def UpperCAmelCase_ ( ):
lowercase = []
for config_class in list(CONFIG_MAPPING.values() ):
lowercase = False
# source code of `config_class`
lowercase = inspect.getsource(__SCREAMING_SNAKE_CASE )
lowercase = _re_checkpoint.findall(__SCREAMING_SNAKE_CASE )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
lowercase , lowercase = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
lowercase = F'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
lowercase = True
break
lowercase = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(sorted(__SCREAMING_SNAKE_CASE ) )
raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 84 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
UpperCAmelCase = '''true'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=16 ):
set_seed(42 )
lowercase = RegressionModel()
lowercase = deepcopy(__SCREAMING_SNAKE_CASE )
lowercase = RegressionDataset(length=__SCREAMING_SNAKE_CASE )
lowercase = DataLoader(__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE )
model.to(accelerator.device )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return model, ddp_model, dataloader
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
lowercase = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
lowercase = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(__SCREAMING_SNAKE_CASE ):
lowercase = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE )
return outputs
with accelerator.main_process_first():
lowercase = dataset.map(
__SCREAMING_SNAKE_CASE , batched=__SCREAMING_SNAKE_CASE , remove_columns=['idx', 'sentence1', 'sentence2'] , )
lowercase = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(__SCREAMING_SNAKE_CASE ):
if use_longest:
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='longest' , return_tensors='pt' )
return tokenizer.pad(__SCREAMING_SNAKE_CASE , padding='max_length' , max_length=128 , return_tensors='pt' )
return DataLoader(__SCREAMING_SNAKE_CASE , shuffle=__SCREAMING_SNAKE_CASE , collate_fn=__SCREAMING_SNAKE_CASE , batch_size=16 )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Accelerator(dispatch_batches=__SCREAMING_SNAKE_CASE , split_batches=__SCREAMING_SNAKE_CASE )
lowercase = get_dataloader(__SCREAMING_SNAKE_CASE , not dispatch_batches )
lowercase = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.prepare(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = []
for batch in dataloader:
lowercase , lowercase = batch.values()
with torch.no_grad():
lowercase = model(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowercase , lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(__SCREAMING_SNAKE_CASE )
targs.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = torch.cat(__SCREAMING_SNAKE_CASE ), torch.cat(__SCREAMING_SNAKE_CASE )
return logits, targs
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=82 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=16 ):
lowercase , lowercase , lowercase = get_basic_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase , lowercase = generate_predictions(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert (
len(__SCREAMING_SNAKE_CASE ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__SCREAMING_SNAKE_CASE )}'''
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False ):
lowercase = evaluate.load('glue' , 'mrpc' )
lowercase , lowercase = get_mrpc_setup(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# First do baseline
lowercase , lowercase , lowercase = setup['no']
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
for batch in dataloader:
batch.to(__SCREAMING_SNAKE_CASE )
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=batch['labels'] )
lowercase = metric.compute()
# Then do distributed
lowercase , lowercase , lowercase = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowercase = model(**__SCREAMING_SNAKE_CASE )
lowercase = outputs.logits.argmax(dim=-1 )
lowercase = batch['labels']
lowercase , lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE )
lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def UpperCAmelCase_ ( ):
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowercase = Accelerator(split_batches=__SCREAMING_SNAKE_CASE , dispatch_batches=__SCREAMING_SNAKE_CASE )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__SCREAMING_SNAKE_CASE , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
lowercase = Accelerator()
test_torch_metrics(__SCREAMING_SNAKE_CASE , 512 )
accelerator.state._reset_state()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 84 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('''input_proj.weight''', '''input_projection.weight'''),
('''input_proj.bias''', '''input_projection.bias'''),
('''query_embed.weight''', '''query_position_embeddings.weight'''),
('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
('''class_embed.weight''', '''class_labels_classifier.weight'''),
('''class_embed.bias''', '''class_labels_classifier.bias'''),
('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
]
)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = state_dict.pop(__SCREAMING_SNAKE_CASE )
lowercase = val
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase = key.replace('backbone.0.body' , 'backbone.conv_encoder.model' )
lowercase = value
else:
lowercase = value
return new_state_dict
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = ''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase = in_proj_weight[:256, :]
lowercase = in_proj_bias[:256]
lowercase = in_proj_weight[256:512, :]
lowercase = in_proj_bias[256:512]
lowercase = in_proj_weight[-256:, :]
lowercase = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase = in_proj_weight[:256, :]
lowercase = in_proj_bias[:256]
lowercase = in_proj_weight[256:512, :]
lowercase = in_proj_bias[256:512]
lowercase = in_proj_weight[-256:, :]
lowercase = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase = in_proj_weight_cross_attn[:256, :]
lowercase = in_proj_bias_cross_attn[:256]
lowercase = in_proj_weight_cross_attn[256:512, :]
lowercase = in_proj_bias_cross_attn[256:512]
lowercase = in_proj_weight_cross_attn[-256:, :]
lowercase = in_proj_bias_cross_attn[-256:]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase , lowercase = image.size
lowercase = max(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = 800 if 'detection' in checkpoint_url else 1000
lowercase = target_max_size / current_max_size
lowercase = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = F.to_tensor(__SCREAMING_SNAKE_CASE )
lowercase = F.normalize(__SCREAMING_SNAKE_CASE , mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] )
return image
@torch.no_grad()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
logger.info('Converting model...' )
# load original state dict
lowercase = torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE , map_location='cpu' )
# rename keys
for src, dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase = rename_backbone_keys(__SCREAMING_SNAKE_CASE )
# query, key and value matrices need special treatment
read_in_q_k_v(__SCREAMING_SNAKE_CASE )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase = 'model.'
for key in state_dict.copy().keys():
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
lowercase = state_dict.pop(__SCREAMING_SNAKE_CASE )
lowercase = val
# create HuggingFace model and load state dict
lowercase = TableTransformerConfig(
backbone='resnet18' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase = 15
lowercase = 2
lowercase = {0: 'table', 1: 'table rotated'}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
else:
lowercase = 125
lowercase = 6
lowercase = {
0: 'table',
1: 'table column',
2: 'table row',
3: 'table column header',
4: 'table projected row header',
5: 'table spanning cell',
}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
lowercase = DetrImageProcessor(
format='coco_detection' , max_size=800 if 'detection' in checkpoint_url else 1000 )
lowercase = TableTransformerForObjectDetection(__SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
model.eval()
# verify our conversion
lowercase = 'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
lowercase = hf_hub_download(repo_id='nielsr/example-pdf' , repo_type='dataset' , filename=__SCREAMING_SNAKE_CASE )
lowercase = Image.open(__SCREAMING_SNAKE_CASE ).convert('RGB' )
lowercase = normalize(resize(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ).unsqueeze(0 )
lowercase = model(__SCREAMING_SNAKE_CASE )
if "detection" in checkpoint_url:
lowercase = (1, 15, 3)
lowercase = torch.tensor(
[[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]] )
lowercase = torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]] )
else:
lowercase = (1, 125, 7)
lowercase = torch.tensor(
[[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]] )
lowercase = torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model to HF hub
logger.info('Pushing model to the hub...' )
lowercase = (
'microsoft/table-transformer-detection'
if 'detection' in checkpoint_url
else 'microsoft/table-transformer-structure-recognition'
)
model.push_to_hub(__SCREAMING_SNAKE_CASE )
image_processor.push_to_hub(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
type=str,
choices=[
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
'''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
],
help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
UpperCAmelCase = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 84 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Any = """OwlViTImageProcessor"""
_UpperCamelCase : Dict = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = 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 , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , snake_case="max_length" , snake_case="np" , **snake_case ):
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(snake_case , snake_case ) or (isinstance(snake_case , snake_case ) and not isinstance(text[0] , snake_case )):
lowercase = [self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )]
elif isinstance(snake_case , snake_case ) and isinstance(text[0] , snake_case ):
lowercase = []
# Maximum number of queries across batch
lowercase = max([len(snake_case ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(snake_case ) != max_num_queries:
lowercase = t + [' '] * (max_num_queries - len(snake_case ))
lowercase = self.tokenizer(snake_case , padding=snake_case , return_tensors=snake_case , **snake_case )
encodings.append(snake_case )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
lowercase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
lowercase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
lowercase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
lowercase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
lowercase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
lowercase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
lowercase = BatchEncoding()
lowercase = input_ids
lowercase = attention_mask
if query_images is not None:
lowercase = BatchEncoding()
lowercase = self.image_processor(
snake_case , return_tensors=snake_case , **snake_case ).pixel_values
lowercase = query_pixel_values
if images is not None:
lowercase = self.image_processor(snake_case , return_tensors=snake_case , **snake_case )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case ) , tensor_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_object_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.image_processor.post_process_image_guided_detection(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
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 SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 84 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = OpenAIGPTTokenizer
_UpperCamelCase : List[Any] = OpenAIGPTTokenizerFast
_UpperCamelCase : int = True
_UpperCamelCase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowercase = dict(zip(snake_case , range(len(snake_case ) ) ) )
lowercase = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase = 'lower'
lowercase = ['low', 'er</w>']
lowercase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
lowercase = tokens + ['<unk>']
lowercase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=15 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
lowercase = 'This is a simple input'
lowercase = ['This is a simple input 1', 'This is a simple input 2']
lowercase = ('This is a simple input', 'This is a pair')
lowercase = [
('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 SCREAMING_SNAKE_CASE__ ( self ):
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A_ ( __lowerCamelCase ):
'''simple docstring'''
pass
| 84 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
UpperCAmelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Dict = VOCAB_FILES_NAMES
_UpperCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self , snake_case=None , snake_case=None , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case="<|endoftext|>" , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=snake_case , merges=snake_case , add_prefix_space=snake_case , trim_offsets=snake_case , ) , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , **snake_case , )
lowercase = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case=None ):
lowercase = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 84 | 1 |
from collections import namedtuple
UpperCAmelCase = namedtuple('''from_to''', '''from_ to''')
UpperCAmelCase = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.001, 1000),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.00454, 264.172),
'''cubicyard''': from_to(0.76455, 1.30795),
'''cubicfoot''': from_to(0.028, 35.3147),
'''cup''': from_to(0.000236588, 4226.75),
}
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if from_type not in METRIC_CONVERSION:
raise ValueError(
F'''Invalid \'from_type\' value: {from_type!r} Supported values are:\n'''
+ ', '.join(__SCREAMING_SNAKE_CASE ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
F'''Invalid \'to_type\' value: {to_type!r}. Supported values are:\n'''
+ ', '.join(__SCREAMING_SNAKE_CASE ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 |
import unittest
from transformers import 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 (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
lowercase = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
lowercase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , head_mask=snake_case )
lowercase = model(snake_case , token_type_ids=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = OpenAIGPTDoubleHeadsModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , *snake_case ):
lowercase = self.num_labels
lowercase = OpenAIGPTForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
_UpperCamelCase : Tuple = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
_UpperCamelCase : str = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case=False ):
lowercase = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=snake_case , )
lowercase = inputs_dict['labels']
lowercase = inputs_dict['labels']
lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=snake_case , )
lowercase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase = OpenAIGPTModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(snake_case )
lowercase = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=snake_case ) # the president is
lowercase = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
lowercase = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].tolist() , snake_case )
| 84 | 1 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowercase = set()
return any(
node not in visited and depth_first_search(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for node in graph )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
visited.add(__SCREAMING_SNAKE_CASE )
rec_stk.add(__SCREAMING_SNAKE_CASE )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(__SCREAMING_SNAKE_CASE )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMSNModel''',
'''ViTMSNForImageClassification''',
'''ViTMSNPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 84 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None ):
lowercase = tesseract_config if tesseract_config is not None else ''
# apply OCR
lowercase = to_pil_image(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = pil_image.size
lowercase = pytesseract.image_to_data(__SCREAMING_SNAKE_CASE , lang=__SCREAMING_SNAKE_CASE , output_type='dict' , config=__SCREAMING_SNAKE_CASE )
lowercase , lowercase , lowercase , lowercase , lowercase = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
lowercase = [idx for idx, word in enumerate(__SCREAMING_SNAKE_CASE ) if not word.strip()]
lowercase = [word for idx, word in enumerate(__SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
lowercase = [coord for idx, coord in enumerate(__SCREAMING_SNAKE_CASE ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowercase = []
for x, y, w, h in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = [x, y, x + w, y + h]
actual_boxes.append(__SCREAMING_SNAKE_CASE )
# finally, normalize the bounding boxes
lowercase = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
assert len(__SCREAMING_SNAKE_CASE ) == len(__SCREAMING_SNAKE_CASE ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""pixel_values"""]
def __init__( self , snake_case = True , snake_case = None , snake_case = PILImageResampling.BILINEAR , snake_case = True , snake_case = None , snake_case = "" , **snake_case , ):
super().__init__(**snake_case )
lowercase = size if size is not None else {'height': 224, 'width': 224}
lowercase = get_size_dict(snake_case )
lowercase = do_resize
lowercase = size
lowercase = resample
lowercase = apply_ocr
lowercase = ocr_lang
lowercase = tesseract_config
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case = PILImageResampling.BILINEAR , snake_case = None , **snake_case , ):
lowercase = get_size_dict(snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
lowercase = (size['height'], size['width'])
return resize(snake_case , size=snake_case , resample=snake_case , data_format=snake_case , **snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = ChannelDimension.FIRST , **snake_case , ):
lowercase = do_resize if do_resize is not None else self.do_resize
lowercase = size if size is not None else self.size
lowercase = get_size_dict(snake_case )
lowercase = resample if resample is not None else self.resample
lowercase = apply_ocr if apply_ocr is not None else self.apply_ocr
lowercase = ocr_lang if ocr_lang is not None else self.ocr_lang
lowercase = tesseract_config if tesseract_config is not None else self.tesseract_config
lowercase = 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:
raise ValueError('Size must be specified if do_resize is True.' )
# All transformations expect numpy arrays.
lowercase = [to_numpy_array(snake_case ) for image in images]
if apply_ocr:
requires_backends(self , 'pytesseract' )
lowercase = []
lowercase = []
for image in images:
lowercase , lowercase = apply_tesseract(snake_case , snake_case , snake_case )
words_batch.append(snake_case )
boxes_batch.append(snake_case )
if do_resize:
lowercase = [self.resize(image=snake_case , size=snake_case , resample=snake_case ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
lowercase = [flip_channel_order(snake_case ) for image in images]
lowercase = [to_channel_dimension_format(snake_case , snake_case ) for image in images]
lowercase = BatchFeature(data={'pixel_values': images} , tensor_type=snake_case )
if apply_ocr:
lowercase = words_batch
lowercase = boxes_batch
return data
| 84 |
import math
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [True] * n
lowercase = False
lowercase = False
lowercase = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
lowercase = i * 2
while index < n:
lowercase = False
lowercase = index + i
lowercase = [2]
for i in range(3 , __SCREAMING_SNAKE_CASE , 2 ):
if is_prime[i]:
primes.append(__SCREAMING_SNAKE_CASE )
return primes
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 9999_6666_3333 ):
lowercase = math.floor(math.sqrt(__SCREAMING_SNAKE_CASE ) ) + 100
lowercase = prime_sieve(__SCREAMING_SNAKE_CASE )
lowercase = 0
lowercase = 0
lowercase = primes[prime_index]
while (last_prime**2) <= limit:
lowercase = primes[prime_index + 1]
lowercase = last_prime**2
lowercase = next_prime**2
# Get numbers divisible by lps(current)
lowercase = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
lowercase = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
lowercase = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
lowercase = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 84 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '''▁'''
UpperCAmelCase = {'''vocab_file''': '''prophetnet.tokenizer'''}
UpperCAmelCase = {
'''vocab_file''': {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
),
}
}
UpperCAmelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}
UpperCAmelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': 512,
}
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = collections.OrderedDict()
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' ) as reader:
lowercase = reader.readlines()
for index, token in enumerate(__SCREAMING_SNAKE_CASE ):
lowercase = token.rstrip('\n' )
lowercase = index
return vocab
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = VOCAB_FILES_NAMES
_UpperCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : Tuple = ["""input_ids""", """attention_mask"""]
def __init__( self , snake_case , snake_case="[SEP]" , snake_case="[SEP]" , snake_case="[SEP]" , snake_case="[UNK]" , snake_case="[PAD]" , snake_case="[CLS]" , snake_case="[MASK]" , snake_case = None , **snake_case , ):
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case , eos_token=snake_case , sep_token=snake_case , unk_token=snake_case , pad_token=snake_case , cls_token=snake_case , mask_token=snake_case , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
' pip install sentencepiece' )
raise
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case ) )
lowercase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
lowercase = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}
for i in range(10 ):
lowercase = F'''[unused{i}]'''
lowercase = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
lowercase = 12
lowercase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(snake_case )
def __getstate__( self ):
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self , snake_case ):
lowercase = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
' pip install sentencepiece' )
raise
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None , snake_case = 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 ([0] * len(snake_case )) + [1]
return ([0] * len(snake_case )) + [1] + ([0] * len(snake_case )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
lowercase = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.sp_model ) + self.fairseq_offset
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = {self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
return self.sp_model.encode(snake_case , out_type=snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase = self.sp_model.PieceToId(snake_case )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = ''.join(snake_case ).replace(snake_case , ' ' ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
if not os.path.isdir(snake_case ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case , 'wb' ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case = None ):
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
lowercase = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 84 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
UpperCAmelCase = re.compile(R'''^\s*try:''')
# Catches a line with else:
UpperCAmelCase = re.compile(R'''^\s*else:''')
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if _re_test_backend.search(__SCREAMING_SNAKE_CASE ) is None:
return None
lowercase = [b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
with open(__SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowercase = f.readlines()
lowercase = 0
while line_index < len(__SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowercase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ):
lowercase = _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE ).groups()[0]
lowercase = re.findall(r'\[([^\]]+)\]' , __SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowercase = _re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowercase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowercase = lines[line_index]
if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE ) is not None:
lowercase = _re_between_brackets.search(__SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowercase = [obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE ) > 0]
objects.extend(__SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(__SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
lowercase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase = []
while (
line_index < len(__SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(__SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowercase = lines[line_index]
lowercase = _re_import.search(__SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
def find_duplicates(__SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase = []
for key in import_dict_objects.keys():
lowercase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
lowercase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase = 'base imports' if key == 'none' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def UpperCAmelCase_ ( ):
lowercase = []
for root, _, files in os.walk(__SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowercase = os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' )
lowercase = parse_init(__SCREAMING_SNAKE_CASE )
if objects is not None:
lowercase = analyze_results(*__SCREAMING_SNAKE_CASE )
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(__SCREAMING_SNAKE_CASE ) )
if len(__SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase_ ( ):
lowercase = []
for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(__SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / folder).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace(os.path.sep , '.' )
submodules.append(__SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowercase = str((Path(__SCREAMING_SNAKE_CASE ) / fname).relative_to(__SCREAMING_SNAKE_CASE ) )
lowercase = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(__SCREAMING_SNAKE_CASE )
return submodules
UpperCAmelCase = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def UpperCAmelCase_ ( ):
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
lowercase = direct_transformers_import(__SCREAMING_SNAKE_CASE )
lowercase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__SCREAMING_SNAKE_CASE , '__init__.py' ) , 'r' ) as f:
lowercase = f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , __SCREAMING_SNAKE_CASE ) ) )
lowercase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__SCREAMING_SNAKE_CASE ) > 0:
lowercase = '\n'.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registed in the main init of Transformers:\n'
F'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 84 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = [0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]
@staticmethod
def SCREAMING_SNAKE_CASE__ ( snake_case , snake_case ):
return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = B'\x80' + B'\x00' * (63 - (len(self.data ) + 8) % 64)
lowercase = self.data + padding + struct.pack('>Q' , 8 * len(self.data ) )
return padded_data
def SCREAMING_SNAKE_CASE__ ( self ):
return [
self.padded_data[i : i + 64] for i in range(0 , len(self.padded_data ) , 64 )
]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = list(struct.unpack('>16L' , snake_case ) ) + [0] * 64
for i in range(16 , 80 ):
lowercase = self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) , 1 )
return w
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.padding()
lowercase = self.split_blocks()
for block in self.blocks:
lowercase = self.expand_block(snake_case )
lowercase , lowercase , lowercase , lowercase , lowercase = self.h
for i in range(0 , 80 ):
if 0 <= i < 20:
lowercase = (b & c) | ((~b) & d)
lowercase = 0X5_a_8_2_7_9_9_9
elif 20 <= i < 40:
lowercase = b ^ c ^ d
lowercase = 0X6_e_d_9_e_b_a_1
elif 40 <= i < 60:
lowercase = (b & c) | (b & d) | (c & d)
lowercase = 0X8_f_1_b_b_c_d_c
elif 60 <= i < 80:
lowercase = b ^ c ^ d
lowercase = 0Xc_a_6_2_c_1_d_6
lowercase , lowercase , lowercase , lowercase , lowercase = (
self.rotate(snake_case , 5 ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
a,
self.rotate(snake_case , 30 ),
c,
d,
)
lowercase = (
self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
)
return ("{:08x}" * 5).format(*self.h )
def UpperCAmelCase_ ( ):
lowercase = b'Test String'
assert SHAaHash(__SCREAMING_SNAKE_CASE ).final_hash() == hashlib.shaa(__SCREAMING_SNAKE_CASE ).hexdigest() # noqa: S324
def UpperCAmelCase_ ( ):
lowercase = argparse.ArgumentParser(description='Process some strings or files' )
parser.add_argument(
'--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument('--file' , dest='input_file' , help='Hash contents of a file' )
lowercase = parser.parse_args()
lowercase = args.input_string
# In any case hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
lowercase = f.read()
else:
lowercase = bytes(__SCREAMING_SNAKE_CASE , 'utf-8' )
print(SHAaHash(__SCREAMING_SNAKE_CASE ).final_hash() )
if __name__ == "__main__":
main()
import doctest
doctest.testmod()
| 84 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar('''T''')
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = data
lowercase = None
def __str__( self ):
return F'''{self.data}'''
class A_ ( Generic[T] ):
'''simple docstring'''
def __init__( self ):
lowercase = None
def __iter__( self ):
lowercase = self.top
while node:
yield node.data
lowercase = node.next
def __str__( self ):
return "->".join([str(snake_case ) for item in self] )
def __len__( self ):
return len(tuple(iter(self ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
return self.top is None
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = Node(snake_case )
if not self.is_empty():
lowercase = self.top
lowercase = node
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , snake_case )
lowercase = self.top
lowercase = self.top.next
return pop_node.data
def SCREAMING_SNAKE_CASE__ ( self ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 84 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=1024 ):
lowercase , lowercase = [], []
lowercase = list(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
lowercase , lowercase = sorted_examples[0]
def is_too_big(__SCREAMING_SNAKE_CASE ):
return tok(__SCREAMING_SNAKE_CASE , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
lowercase = new_src + ' ' + src
lowercase = new_tgt + ' ' + tgt
if is_too_big(__SCREAMING_SNAKE_CASE ) or is_too_big(__SCREAMING_SNAKE_CASE ): # cant fit, finalize example
finished_src.append(__SCREAMING_SNAKE_CASE )
finished_tgt.append(__SCREAMING_SNAKE_CASE )
lowercase , lowercase = src, tgt
else: # can fit, keep adding
lowercase , lowercase = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__SCREAMING_SNAKE_CASE )
finished_tgt.append(__SCREAMING_SNAKE_CASE )
return finished_src, finished_tgt
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = Path(__SCREAMING_SNAKE_CASE )
save_path.mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
for split in ["train"]:
lowercase , lowercase = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
lowercase = [x.rstrip() for x in Path(__SCREAMING_SNAKE_CASE ).open().readlines()]
lowercase = [x.rstrip() for x in Path(__SCREAMING_SNAKE_CASE ).open().readlines()]
lowercase , lowercase = pack_examples(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
print(F'''packed {split} split from {len(__SCREAMING_SNAKE_CASE )} examples -> {len(__SCREAMING_SNAKE_CASE )}.''' )
Path(save_path / F'''{split}.source''' ).open('w' ).write('\n'.join(__SCREAMING_SNAKE_CASE ) )
Path(save_path / F'''{split}.target''' ).open('w' ).write('\n'.join(__SCREAMING_SNAKE_CASE ) )
for split in ["val", "test"]:
lowercase , lowercase = data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
shutil.copyfile(__SCREAMING_SNAKE_CASE , save_path / F'''{split}.source''' )
shutil.copyfile(__SCREAMING_SNAKE_CASE , save_path / F'''{split}.target''' )
def UpperCAmelCase_ ( ):
lowercase = argparse.ArgumentParser()
parser.add_argument('--tok_name' , type=__SCREAMING_SNAKE_CASE , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' , type=__SCREAMING_SNAKE_CASE , default=128 )
parser.add_argument('--data_dir' , type=__SCREAMING_SNAKE_CASE )
parser.add_argument('--save_path' , type=__SCREAMING_SNAKE_CASE )
lowercase = parser.parse_args()
lowercase = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__SCREAMING_SNAKE_CASE , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 84 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=False , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
lowercase = parent
lowercase = batch_size
lowercase = seq_length
lowercase = is_training
lowercase = use_input_mask
lowercase = use_token_type_ids
lowercase = use_labels
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = type_sequence_label_size
lowercase = initializer_range
lowercase = num_labels
lowercase = num_choices
lowercase = scope
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase = None
if self.use_input_mask:
lowercase = random_attention_mask([self.batch_size, self.seq_length] )
lowercase = None
if self.use_token_type_ids:
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase = None
lowercase = None
lowercase = None
if self.use_labels:
lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase = ids_tensor([self.batch_size] , self.num_choices )
lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self ):
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ):
lowercase = LlamaModel(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case )
lowercase = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = LlamaModel(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , )
lowercase = model(snake_case , attention_mask=snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
lowercase = True
lowercase = True
lowercase = LlamaForCausalLM(config=snake_case )
model.to(snake_case )
model.eval()
# first forward pass
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , use_cache=snake_case , )
lowercase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
lowercase = model(
snake_case , attention_mask=snake_case , encoder_hidden_states=snake_case , encoder_attention_mask=snake_case , past_key_values=snake_case , output_hidden_states=snake_case , )['hidden_states'][0]
# select random slice
lowercase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.prepare_config_and_inputs()
(
(
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) , (
lowercase
) ,
) = config_and_inputs
lowercase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase : Tuple = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
_UpperCamelCase : List[Any] = (LlamaForCausalLM,) if is_torch_available() else ()
_UpperCamelCase : int = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCamelCase : int = False
_UpperCamelCase : int = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = LlamaModelTester(self )
lowercase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase = type
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'single_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = 3
lowercase = 'multi_label_classification'
lowercase = input_dict['input_ids']
lowercase = input_ids.ne(1 ).to(snake_case )
lowercase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
lowercase = LlamaForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
lowercase = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def SCREAMING_SNAKE_CASE__ ( self ):
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = ids_tensor([1, 10] , config.vocab_size )
lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = LlamaModel(snake_case )
original_model.to(snake_case )
original_model.eval()
lowercase = original_model(snake_case ).last_hidden_state
lowercase = original_model(snake_case ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
lowercase = {'type': scaling_type, 'factor': 10.0}
lowercase = LlamaModel(snake_case )
scaled_model.to(snake_case )
scaled_model.eval()
lowercase = scaled_model(snake_case ).last_hidden_state
lowercase = scaled_model(snake_case ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
@require_torch
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
lowercase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
# Expected mean on dim = -1
lowercase = torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]] )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
lowercase = torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [1, 306, 4658, 278, 6593, 310, 2834, 338]
lowercase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
lowercase = model(torch.tensor(snake_case ) )
lowercase = torch.tensor(
[[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , snake_case , atol=1E-2 , rtol=1E-2 )
# fmt: off
lowercase = torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , snake_case , atol=1E-5 , rtol=1E-5 )
@unittest.skip('Model is curently gated' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
lowercase = 'Simply put, the theory of relativity states that '
lowercase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
lowercase = tokenizer.encode(snake_case , return_tensors='pt' )
lowercase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=snake_case )
# greedy generation outputs
lowercase = model.generate(snake_case , max_new_tokens=64 , top_p=snake_case , temperature=1 , do_sample=snake_case )
lowercase = tokenizer.decode(generated_ids[0] , skip_special_tokens=snake_case )
self.assertEqual(snake_case , snake_case )
| 84 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
# Construct model
if gpta_config_file == "":
lowercase = GPTaConfig()
else:
lowercase = GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
lowercase = GPTaModel(__SCREAMING_SNAKE_CASE )
# Load weights from numpy
load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Save pytorch-model
lowercase = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
lowercase = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , __SCREAMING_SNAKE_CASE )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(__SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--gpt2_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
UpperCAmelCase = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 84 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class A_ :
'''simple docstring'''
_UpperCamelCase : Dict = """dummy_data"""
_UpperCamelCase : Optional[int] = """datasets"""
_UpperCamelCase : Tuple = False
def __init__( self , snake_case , snake_case , snake_case , snake_case = None , snake_case = False , snake_case = True , snake_case = None , ):
lowercase = 0
lowercase = dataset_name
lowercase = cache_dir
lowercase = use_local_dummy_data
lowercase = config
# download_callbacks take a single url as input
lowercase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase = str(snake_case )
# to be downloaded
lowercase = None
lowercase = None
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._dummy_file is None:
lowercase = self.download_dummy_data()
return self._dummy_file
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase = cached_path(
snake_case , cache_dir=self.cache_dir , extract_compressed_file=snake_case , force_extract=snake_case )
return os.path.join(snake_case , self.dummy_file_name )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
if self._bucket_url is None:
lowercase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def SCREAMING_SNAKE_CASE__ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case , snake_case ):
return self.create_dummy_data_dict(snake_case , snake_case )
elif isinstance(snake_case , (list, tuple) ):
return self.create_dummy_data_list(snake_case , snake_case )
else:
return self.create_dummy_data_single(snake_case , snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
return self.download_and_extract(snake_case )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , *snake_case , **snake_case ):
return path
def SCREAMING_SNAKE_CASE__ ( self ):
return {}
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case , snake_case ):
for single_url in single_urls:
download_callback(snake_case )
else:
lowercase = single_urls
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(snake_case , snake_case ):
lowercase = [os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) ) for x in single_urls]
else:
lowercase = single_urls
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(Path(snake_case ).name ) )
lowercase = value
# make sure that values are unique
if all(isinstance(snake_case , snake_case ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , snake_case ) ) for url in data_url )
lowercase = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase = [data_url[0]] * len(snake_case )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(snake_case )
return dummy_data_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
for download_callback in self.download_callbacks:
download_callback(snake_case )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase = os.path.join(snake_case , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(snake_case ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self ):
pass
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
def _iter_archive_members(snake_case ):
# this preserves the order of the members inside the ZIP archive
lowercase = Path(self.dummy_file ).parent
lowercase = path.relative_to(snake_case )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case )
lowercase = Path(snake_case )
lowercase = _iter_archive_members(snake_case ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(snake_case ).as_posix(), file_path.open('rb' )
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
lowercase = [paths]
for path in paths:
if os.path.isfile(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case ):
if os.path.basename(snake_case ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(snake_case ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(snake_case , snake_case )
| 84 | 1 |
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