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gpt2-common-sense-reasoning

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gpt2-common-sense-reasoning / model /__init__.py

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add weights and tokenizers

282c159 almost 3 years ago

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	# flake8: noqa
	# There's no way to ignore "F401 '...' imported but unused" warnings in this
	# module, but to preserve other warnings. So, don't check this module at all.

	# Copyright 2020 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	from typing import TYPE_CHECKING

	from ...file_utils import (
	_BaseLazyModule,
	is_flax_available,
	is_tf_available,
	is_tokenizers_available,
	is_torch_available,
	)


	_import_structure = {
	"configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config"],
	"tokenization_gpt2": ["GPT2Tokenizer"],
	}

	if is_tokenizers_available():
	_import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]

	if is_torch_available():
	_import_structure["modeling_gpt2"] = [
	"GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
	"GPT2DoubleHeadsModel",
	"GPT2ForSequenceClassification",
	"GPT2LMHeadModel",
	"GPT2Model",
	"GPT2PreTrainedModel",
	"load_tf_weights_in_gpt2",
	]

	if is_tf_available():
	_import_structure["modeling_tf_gpt2"] = [
	"TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
	"TFGPT2DoubleHeadsModel",
	"TFGPT2ForSequenceClassification",
	"TFGPT2LMHeadModel",
	"TFGPT2MainLayer",
	"TFGPT2Model",
	"TFGPT2PreTrainedModel",
	]

	if is_flax_available():
	_import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

	if TYPE_CHECKING:
	from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
	from .tokenization_gpt2 import GPT2Tokenizer

	if is_tokenizers_available():
	from .tokenization_gpt2_fast import GPT2TokenizerFast

	if is_torch_available():
	from .modeling_gpt2 import (
	GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
	GPT2DoubleHeadsModel,
	GPT2ForSequenceClassification,
	GPT2LMHeadModel,
	GPT2Model,
	GPT2PreTrainedModel,
	load_tf_weights_in_gpt2,
	)

	if is_tf_available():
	from .modeling_tf_gpt2 import (
	TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
	TFGPT2DoubleHeadsModel,
	TFGPT2ForSequenceClassification,
	TFGPT2LMHeadModel,
	TFGPT2MainLayer,
	TFGPT2Model,
	TFGPT2PreTrainedModel,
	)

	if is_flax_available():
	from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel

	else:
	import importlib
	import os
	import sys

	class _LazyModule(_BaseLazyModule):
	"""
	Module class that surfaces all objects but only performs associated imports when the objects are requested.
	"""

	__file__ = globals()["__file__"]
	__path__ = [os.path.dirname(__file__)]

	def _get_module(self, module_name: str):
	return importlib.import_module("." + module_name, self.__name__)

	sys.modules[__name__] = _LazyModule(__name__, _import_structure)