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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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import importlib.util import json import os import shutil import sys import warnings from collections import OrderedDict from functools import lru_cache, wraps from itertools import chain from types import ModuleType from typing import Any from packaging import version from transformers.utils.versions import importlib_...
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from ..utils import DummyObject, requires_backends def convert_slow_tokenizer(*args, **kwargs): requires_backends(convert_slow_tokenizer, ["sentencepiece", "tokenizers"])
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Tests if `x` is a `torch.Tensor`, `tf.Tensor`, `jaxlib.xla_extension.DeviceArray` or `np.ndarray`.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Tests if `x` is a torch device or not. Safe to call even if torch is not installed.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Find the labels used by a given model. Args: model_class (`type`): The class of the model.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Flatten a nested dict into a single level dict.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
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import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
11,613
import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import i...
Framework-agnostic version of `numpy.size` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Return a logger with the specified name. This function is not supposed to be directly accessed unless you are writing a custom transformers module.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Return the current level for the 🤗 Transformers's root logger as an int. Returns: `int`: The logging level. <Tip> 🤗 Transformers has following logging levels: - 50: `transformers.logging.CRITICAL` or `transformers.logging.FATAL` - 40: `transformers.logging.ERROR` - 30: `transformers.logging.WARNING` or `transformers....
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Set the verbosity to the `INFO` level.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Set the verbosity to the `WARNING` level.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Set the verbosity to the `DEBUG` level.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Set the verbosity to the `ERROR` level.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Disable the default handler of the HuggingFace Transformers's root logger.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Enable the default handler of the HuggingFace Transformers's root logger.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
adds a handler to the HuggingFace Transformers's root logger.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
removes given handler from the HuggingFace Transformers's root logger.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Disable propagation of the library log outputs. Note that log propagation is disabled by default.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Enable propagation of the library log outputs. Please disable the HuggingFace Transformers's default handler to prevent double logging if the root logger has been configured.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Enable explicit formatting for every HuggingFace Transformers's logger. The explicit formatter is as follows: ``` [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE ``` All handlers currently bound to the root logger are affected by this method.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Resets the formatting for HuggingFace Transformers's loggers. All handlers currently bound to the root logger are affected by this method.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
This method is identical to `logger.warning()`, but if env var TRANSFORMERS_NO_ADVISORY_WARNINGS=1 is set, this warning will not be printed
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Return a boolean indicating whether tqdm progress bars are enabled.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Enable tqdm progress bar.
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import logging import os import sys import threading from logging import CRITICAL from logging import DEBUG from logging import ERROR from logging import FATAL from logging import INFO from logging import NOTSET from logging import WARN from logging import WARNING from typing import Optional from tqdm import au...
Disable tqdm progress bar.
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import functools import re import types def add_start_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "") return fn return docstring_decorator
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import functools import re import types def add_start_docstrings_to_model_forward(*docstr): def docstring_decorator(fn): docstring = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "") class_name = f"[`{fn.__qualname__.split('.')[0]}`]" intro = f" The {class_name} forward met...
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import functools import re import types def add_end_docstrings(*docstr): def docstring_decorator(fn): fn.__doc__ = (fn.__doc__ if fn.__doc__ is not None else "") + "".join(docstr) return fn return docstring_decorator
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import functools import re import types def _prepare_output_docstrings(output_type, config_class, min_indent=None): """ Prepares the return part of the docstring using `output_type`. """ output_docstring = output_type.__doc__ # Remove the head of the docstring to keep the list of args only lines...
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import functools import re import types def _get_indent(t): """Returns the indentation in the first line of t""" search = re.search(r"^(\s*)\S", t) return "" if search is None else search.groups()[0] def _prepare_output_docstrings(output_type, config_class, min_indent=None): """ Prepares the return ...
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import functools import re import types The provided code snippet includes necessary dependencies for implementing the `copy_func` function. Write a Python function `def copy_func(f)` to solve the following problem: Returns a copy of a function f. Here is the function: def copy_func(f): """Returns a copy of a fu...
Returns a copy of a function f.
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from ..utils import DummyObject, requires_backends def load_tf_weights_in_tapas(*args, **kwargs): requires_backends(load_tf_weights_in_tapas, ["scatter"])
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
Returns the underlying metadata for HFProxies, and behaves like the identity for the others.
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
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import builtins import collections import functools import inspect import math import operator import os import random import warnings from typing import Any, Callable, Dict, List, Optional, Type, Union import torch from packaging import version from torch import nn from torch.fx import Graph, GraphModule, Proxy, Trace...
Performs symbolic tracing on the model. Args: model ([`PretrainedModel`]): The model to trace. input_names (`List[str]`, *optional*): The names of the inputs of the traced model. If unset, model.dummy_inputs.keys() are used instead. disable_check (`bool`, *optional*, defaults to `False`): If `True`, no check is done be...
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from math import ceil def assert_device_map(device_map, num_blocks): blocks = list(range(0, num_blocks)) device_map_blocks = [item for sublist in list(device_map.values()) for item in sublist] # Duplicate check duplicate_blocks = [] for i in device_map_blocks: if device_map_blocks.count(i...
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from math import ceil The provided code snippet includes necessary dependencies for implementing the `get_device_map` function. Write a Python function `def get_device_map(n_layers, devices)` to solve the following problem: Returns a dictionary of layers distributed evenly across all devices. Here is the function: d...
Returns a dictionary of layers distributed evenly across all devices.
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from copy import deepcopy from transformers.utils import is_accelerate_available, is_bitsandbytes_available The provided code snippet includes necessary dependencies for implementing the `replace_8bit_linear` function. Write a Python function `def replace_8bit_linear(model, threshold=6.0, modules_to_not_convert="lm_he...
A helper function to replace all `torch.nn.Linear` modules by `bnb.nn.Linear8bit` modules from the `bitsandbytes` library. This will enable running your models using mixed int8 precision as described by the paper `GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale`. Make sure `bitsandbytes` compiled wit...
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from copy import deepcopy from transformers.utils import is_accelerate_available, is_bitsandbytes_available The provided code snippet includes necessary dependencies for implementing the `get_keys_to_not_convert` function. Write a Python function `def get_keys_to_not_convert(model)` to solve the following problem: r""...
r""" An utility function to get the key of the module to keep in full precision if any For example for CausalLM modules we may want to keep the lm_head in full precision for numerical stability reasons. For other architectures, we want to keep the tied weights of the model. The function will return a list of the keys o...
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from ..utils import DummyObject, requires_backends def tf_top_k_top_p_filtering(*args, **kwargs): requires_backends(tf_top_k_top_p_filtering, ["tf"])
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from ..utils import DummyObject, requires_backends def shape_list(*args, **kwargs): requires_backends(shape_list, ["tf"])
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from ..utils import DummyObject, requires_backends def create_optimizer(*args, **kwargs): requires_backends(create_optimizer, ["tf"])
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import collections import json import logging import os from typing import Optional, Tuple import numpy as np from tqdm.auto import tqdm logger = logging.getLogger(__name__) The provided code snippet includes necessary dependencies for implementing the `postprocess_qa_predictions_with_beam_search` function. Write a Py...
Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the original contexts. This is the postprocessing functions for models that return start and end logits, indices, as well as cls token predictions. Args: examples: The non-preprocessed dataset...
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import csv from collections import defaultdict from dataclasses import dataclass, field from typing import List, Optional import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import ScalarFormatter from transformers import HfArgumentParser def list_field(default=None, metadata=None): return fi...
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import csv from collections import defaultdict from dataclasses import dataclass, field from typing import List, Optional import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import ScalarFormatter from transformers import HfArgumentParser def can_convert_to_int(string): try: int(strin...
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import csv from collections import defaultdict from dataclasses import dataclass, field from typing import List, Optional import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import ScalarFormatter from transformers import HfArgumentParser def can_convert_to_float(string): try: float(s...
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