id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,556 | import inspect
from typing import Callable, List, Optional, Set, Tuple, Union
import torch
from packaging import version
from torch import _softmax_backward_data, nn
from .utils import logging
The provided code snippet includes necessary dependencies for implementing the `find_pruneable_heads_and_indices` function. Wr... | Finds the heads and their indices taking `already_pruned_heads` into account. Args: heads (`List[int]`): List of the indices of heads to prune. n_heads (`int`): The number of heads in the model. head_size (`int`): The size of each head. already_pruned_heads (`Set[int]`): A set of already pruned heads. Returns: `Tuple[S... |
10,557 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
class TrainCommand(BaseTransformersC... | Factory function used to instantiate training command from provided command line arguments. Returns: TrainCommand |
10,558 | import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional
import requests
from ..utils import logging
from . import BaseTransformersCLICommand
The provided code snippet includes necessary ... | Write out the message in Line delimited JSON. |
10,559 | import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional
import requests
from ..utils import logging
from . import BaseTransformersCLICommand
logger = logging.get_logger(__name__)
The pro... | Read Line delimited JSON from stdin. |
10,560 | import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
class AddNewModelCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParse... | null |
10,561 | from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
class ConvertCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
... | Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. Returns: ServeCommand |
10,562 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
def try_infer_format_from_ext(path: str):
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMAT... | null |
10,563 | import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module
import numpy as np
from packaging import version
import huggingface_hub
from .. import (
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
... | Factory function used to convert a model PyTorch checkpoint in a TensorFlow 2 checkpoint. Returns: ServeCommand |
10,564 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
def Body(*x, **y):
pass | null |
10,565 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
class ServeCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
... | Factory function used to instantiate serving server from provided command line arguments. Returns: ServeCommand |
10,566 | from argparse import ArgumentParser
from . import BaseTransformersCLICommand
class DownloadCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("download")
download_parser.add_argument(
"--cache-dir", type=str, default=... | null |
10,567 | import subprocess
from argparse import ArgumentParser
from typing import List, Union
from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError
from . import BaseTransformersCLICommand
The provided code snippet includes necessary dependencies for implementing the `tabula... | Inspired by: - stackoverflow.com/a/8356620/593036 - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data |
10,568 | import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_flax_available, is_tf_available, is_torch_available
from . import BaseTransformersCLICommand
class EnvironmentCommand(BaseTransformersCLICommand):
def register_subcommand(parser: A... | null |
10,569 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as ... | Creates a new model module like a given model of the Transformers library. Args: model_type (`str`): The model type to duplicate (like "bert" or "gpt2") new_model_patterns (`ModelPatterns`): The patterns for the new model. add_copied_from (`bool`, *optional*, defaults to `True`): Whether or not to add "Copied from" sta... |
10,570 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as ... | null |
10,571 | import difflib
import json
import os
import re
from argparse import ArgumentParser, Namespace
from dataclasses import dataclass
from datetime import date
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Pattern, Tuple, Union
import transformers.models.auto as ... | Ask the user for the necessary inputs to add the new model. |
10,572 | import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from .utils import add_start_docstrings
class MaxLengthCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. K... | null |
10,573 | import collections
from .utils import ExplicitEnum, is_torch_available, logging
def get_abs_min_max(var, ctx):
abs_var = var.abs()
return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}" | null |
10,574 | import collections
from .utils import ExplicitEnum, is_torch_available, logging
The provided code snippet includes necessary dependencies for implementing the `detect_overflow` function. Write a Python function `def detect_overflow(var, ctx)` to solve the following problem:
Report whether the tensor contains any `nan`... | Report whether the tensor contains any `nan` or `inf` entries. This is useful for detecting overflows/underflows and best to call right after the function that did some math that modified the tensor in question. This function contains a few other helper features that you can enable and tweak directly if you want to tra... |
10,575 | import warnings
from typing import Dict, List, Tuple
from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from .utils import requires_backends
def check_number_comma(piece: str) -> bool:
return len(piece) < 2 or piece[-1] !... | null |
10,576 | import warnings
from typing import Dict, List, Tuple
from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from .utils import requires_backends
SLOW_TO_FAST_CONVERTERS = {
"AlbertTokenizer": AlbertConverter,
"BartTokenize... | Utilities to convert a slow tokenizer instance in a fast tokenizer instance. Args: transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]): Instance of a slow tokenizer to convert in the backend tokenizer for [`~tokenization_utils_base.PreTrainedTokenizerFast`]. Return: A instance of [`~tokenizers.Toke... |
10,577 | import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
"""
Applies a warmup schedule on a given learning rate decay schedule.
Args:
initial_learning_rate (`float`):
The initial learning rate f... | Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. Args: init_lr (`float`): The desired learning rate at the end of the warmup phase. num_train_steps (`int`): The total number of training steps. num_warmup_steps (`int`): The number of warmup steps. min_lr_ratio (`float`,... |
10,578 | import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_n... | null |
10,579 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import (... | null |
10,580 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import (... | null |
10,581 | import os
from typing import TYPE_CHECKING, List, Tuple, Union
import numpy as np
from packaging import version
import requests
from .utils import (
ExplicitEnum,
is_jax_tensor,
is_tf_tensor,
is_torch_available,
is_torch_tensor,
is_vision_available,
to_numpy,
)
from .utils.constants import (... | Loads `image` to a PIL Image. Args: image (`str` or `PIL.Image.Image`): The image to convert to the PIL Image format. Returns: `PIL.Image.Image`: A PIL Image. |
10,582 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,583 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,584 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,585 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,586 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,587 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,588 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | Parse the `logs` of either a `tf.keras.History` object returned by `model.fit()` or an accumulated logs `dict` passed to the `PushToHubCallback`. Returns lines and logs compatible with those returned by `parse_log_history`. |
10,589 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | Parse the `log_history` of a Trainer to get the intermediate and final evaluation results. |
10,590 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,591 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | Create a nice Markdown table from the results in `lines`. |
10,592 | import copy
import json
import os
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
import yaml
from huggingface_hub import model_info
from huggingface_hub.utils import HFValidationError
from . import __version__
from .models.a... | null |
10,593 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization i... | null |
10,594 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization i... | Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a given size. The sub-checkpoints are determined by iterating through the `state_dict` in the order of its keys, so there is no optimization made to make each sub-checkpoint as close as possible to the maxim... |
10,595 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization i... | null |
10,596 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization i... | null |
10,597 | import gc
import json
import os
import re
from functools import partial
from pickle import UnpicklingError
from typing import Any, Dict, Set, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
import msgpack.exceptions
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.serialization i... | null |
10,598 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a constant learning rate, using the learning rate set in optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. Return: `torch.optim.lr_sche... |
10,599 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_warmup_steps (`int`): The number of st... |
10,600 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. num_w... |
10,601 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for ... |
10,602 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Opti... |
10,603 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
The... | Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the optimizer to end lr defined by *lr_end*, after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for whic... |
10,604 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
TYPE... | Unified API to get any scheduler from its name. Args: name (`str` or `SchedulerType`): The name of the scheduler to use. optimizer (`torch.optim.Optimizer`): The optimizer that will be used during training. num_warmup_steps (`int`, *optional*): The number of warmup steps to do. This is not required by all schedulers (h... |
10,605 | import math
import warnings
from typing import Callable, Iterable, Optional, Tuple, Union
import torch
from torch import nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
clas... | Get a proxy schedule for [`~optimization.Adafactor`] Args: optimizer ([`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. initial_lr (`float`, *optional*, defaults to 0.0): Initial lr Return: [`~optimization.Adafactor`] proxy schedule object. |
10,606 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Concat the `new_tensors` to `tensors` on the first dim and pad them on the second if needed. Works for tensors or nested list/tuples/dict of tensors. |
10,607 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Find the first dimension of a tensor in a nested list/tuple/dict of tensors. |
10,608 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Numpify `tensors` (even if it's a nested list/tuple/dict of tensors). |
10,609 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Detach `tensors` (even if it's a nested list/tuple/dict of tensors). |
10,610 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,611 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,612 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,613 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,614 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Decorator to make all processes in distributed training wait for each local_master to do something. Args: local_rank (`int`): The rank of the local process. |
10,615 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,616 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Create the same nested structure as `arrays` with a first dimension always at `num_samples`. |
10,617 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Expand the `arrays` so that the second dimension grows to `new_seq_length`. Uses `padding_index` for padding. |
10,618 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Truncate `tensors` at `limit` (even if it's a nested list/tuple/dict of tensors). |
10,619 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar lengths. To do this, the indices are: - randomly permuted - grouped in mega-batches of size `mega_batch_mult * batch_size` - sorted by length in each mega-batch The result is the concatenation of all mega-b... |
10,620 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,621 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Log metrics in a specially formatted way Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predictmetrics: metrics dict Notes on memory reports: In order t... |
10,622 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Save metrics into a json file for that split, e.g. `train_results.json`. Under distributed environment this is done only for a process with rank 0. Args: split (`str`): Mode/split name: one of `train`, `eval`, `test`, `all` metrics (`Dict[str, float]`): The metrics returned from train/evaluate/predict combined (`bool`,... |
10,623 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model Under distributed environment this is done only for a process with rank 0. |
10,624 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Returns the names of the model parameters that are not inside a forbidden layer. |
10,625 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | Gets a class from a module by its name. Args: module (`torch.nn.Module`): The module to get the class from. name (`str`): The name of the class. |
10,626 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,627 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,628 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,629 | import datetime
import json
import math
import os
import sys
import warnings
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass
from logging import StreamHandler
from typing import Any, Dict, Iterator, List, Optional, Union
import numpy as np
import torch
import ... | null |
10,630 | import inspect
import math
from typing import Callable, Iterable, List, Optional, Tuple
import numpy as np
import torch
from .utils import add_start_docstrings
from .utils.logging import get_logger
def _get_ngrams(ngram_size: int, prev_input_ids: torch.Tensor, num_hypos: int):
generated_ngrams = [{} for _ in range(... | Copied from fairseq for no_repeat_ngram in beam_search |
10,631 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score =... | null |
10,632 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
def get_raw_scores(examples, preds):
"""
Computes the exact and f1 scores from the examples and the model predictions
"""
exact_scores = {}
f1_scores = {}
for... | null |
10,633 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original tex... | Write final predictions to the json file and log-odds of null if needed. |
10,634 | import collections
import json
import math
import re
import string
from ...models.bert import BasicTokenizer
from ...utils import logging
logger = logging.get_logger(__name__)
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original tex... | XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of null if needed. Requires utils_squad_evaluate.py |
10,635 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import... | Very simple data collator that simply collates batches of dict-like objects and performs special handling for potential keys named: - `label`: handles a single value (int or float) per object - `label_ids`: handles a list of values per object Does not do any additional preprocessing: property names of the input object ... |
10,636 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import... | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,637 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import... | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,638 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import... | Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary. |
10,639 | import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from ..models.bert import BertTokenizer, BertTokenizerFast
from ..tokenization_utils_base import... | null |
10,640 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import... | Check if this is the 'max context' doc span for the token. |
10,641 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import... | null |
10,642 | import json
import os
from functools import partial
from multiprocessing import Pool, cpu_count
import numpy as np
from tqdm import tqdm
from ...models.bert.tokenization_bert import whitespace_tokenize
from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
from ...utils import... | Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs. Args: examples: list of [`~data.processors.squad.SquadExample`] tokenizer: an instance of a child of [`PreTraine... |
10,643 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def set_hf_deepspeed_config(hf_deepspeed_config_obj):
... | null |
10,644 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def unset_hf_deepspeed_config():
# useful for unit... | null |
10,645 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
_hf_deepspeed_config_weak_ref = None
def deepspeed_config():
if _hf_deepspeed_config_we... | null |
10,646 | import importlib.util
import weakref
from copy import deepcopy
from functools import partialmethod
from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging
logger = logging.get_logger(__name__)
def deepspeed_optim_sched(trainer, hf_deepspeed_config... | Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args. If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made. Args: trainer: Trainer object num_training_steps: per single gpu resume_from_checkpoint: path to a checkpoint if ... |
10,647 | import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, model_info
from .utils import HF_MODULES_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, cached_file, is_offline_mode, logging
def get_class_in_module(class_name... | Extracts a class from a module file, present in the local folder or repository of a model. <Tip warning={true}> Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should therefore only be called on trusted repos. </Tip> Args: pretrained_model_name_or_path (`str` ... |
10,648 | import importlib
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from huggingface_hub import HfFolder, model_info
from .utils import HF_MODULES_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, cached_file, is_offline_mode, logging
logger = logging.get_logger(__name... | Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally adds the proper fields in a config. Args: obj (`Any`): The object for which to save the module files. folder (`str` or `os.PathLike`): The folder where to save. config (`PretrainedConfig` or dictionary, `o... |
10,649 | import math
from collections import OrderedDict
import torch
from packaging import version
from torch import Tensor, nn
from .utils import logging
ACT2FN = ClassInstantier(ACT2CLS)
def get_activation(activation_string):
if activation_string in ACT2FN:
return ACT2FN[activation_string]
else:
rais... | null |
10,650 | import subprocess
from typing import Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
The provided code snippet includes necessary dependencies for implementing the `ffmpeg_read` function. Write a Python function `def ffmpeg_rea... | Helper function to read an audio file through ffmpeg. |
10,651 | import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `ffmpeg_read` function. Write a Python function `def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array` to solve the following problem... | Helper function to read an audio file through ffmpeg. |
10,652 | import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def ffmpeg_microphone(
sampling_rate: int,
chunk_length_s: float,
format_for_conversion: str = "f32le",
):
"""
Helper function ro read raw microphone data.
"""
ar = f"{sampling_rate}"
ac = "1"
... | Helper function to read audio from the microphone file through ffmpeg. This will output `partial` overlapping chunks starting from `stream_chunk_s` (if it is defined) until `chunk_length_s` is reached. It will make use of striding to avoid errors on the "sides" of the various chunks. Arguments: sampling_rate (`int`): T... |
10,653 | import re
from typing import List, Optional, Tuple, Union
import numpy as np
from ..utils import (
ExplicitEnum,
add_end_docstrings,
is_pytesseract_available,
is_torch_available,
is_vision_available,
logging,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
from .question_answering import s... | Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes. |
10,654 | import types
import warnings
from collections.abc import Iterable
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import numpy as np
from ..data import SquadExample, SquadFeatures, squad_convert_examples_to_features
from ..modelcard import ModelCard
from ..tokenization_utils import PreTrainedTokeni... | Takes the raw output of any `ModelForQuestionAnswering` and first normalizes its outputs and then uses `decode_spans()` to generate probabilities for each span to be the actual answer. Args: start (`np.ndarray`): Individual start logits for each token. end (`np.ndarray`): Individual end logits for each token. p_mask (`... |
10,655 | import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
def sigmoid(_outputs):
return 1.0 / (1.0 + np.exp(-_outputs)) | null |
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