




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_		:				str									=     {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				List[str]									=     ["RobertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Dict									=     [
							    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "RobertaForCausalLM",
							    "RobertaForMaskedLM",
							    "RobertaForMultipleChoice",
							    "RobertaForQuestionAnswering",
							    "RobertaForSequenceClassification",
							    "RobertaForTokenClassification",
							    "RobertaModel",
							    "RobertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Dict									=     [
							    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFRobertaForCausalLM",
							    "TFRobertaForMaskedLM",
							    "TFRobertaForMultipleChoice",
							    "TFRobertaForQuestionAnswering",
							    "TFRobertaForSequenceClassification",
							    "TFRobertaForTokenClassification",
							    "TFRobertaMainLayer",
							    "TFRobertaModel",
							    "TFRobertaPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[Any]									=     [
							    "FlaxRobertaForCausalLM",
							    "FlaxRobertaForMaskedLM",
							    "FlaxRobertaForMultipleChoice",
							    "FlaxRobertaForQuestionAnswering",
							    "FlaxRobertaForSequenceClassification",
							    "FlaxRobertaForTokenClassification",
							    "FlaxRobertaModel",
							    "FlaxRobertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
							from .tokenization_roberta import RobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roberta_fast import RobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta import (
														    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaForCausalLM,
														    RobertaForMaskedLM,
														    RobertaForMultipleChoice,
														    RobertaForQuestionAnswering,
														    RobertaForSequenceClassification,
														    RobertaForTokenClassification,
														    RobertaModel,
														    RobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta import (
														    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaForCausalLM,
														    TFRobertaForMaskedLM,
														    TFRobertaForMultipleChoice,
														    TFRobertaForQuestionAnswering,
														    TFRobertaForSequenceClassification,
														    TFRobertaForTokenClassification,
														    TFRobertaMainLayer,
														    TFRobertaModel,
														    TFRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta import (
														    FlaxRobertaForCausalLM,
														    FlaxRobertaForMaskedLM,
														    FlaxRobertaForMultipleChoice,
														    FlaxRobertaForQuestionAnswering,
														    FlaxRobertaForSequenceClassification,
														    FlaxRobertaForTokenClassification,
														    FlaxRobertaModel,
														    FlaxRobertaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_		:				Tuple									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase_		:				str									=     "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
							import torch

							print("Torch version:", torch.__version__)
							print("Cuda available:", torch.cuda.is_available())
							print("Cuda version:", torch.version.cuda)
							print("CuDNN version:", torch.backends.cudnn.version())
							print("Number of GPUs available:", torch.cuda.device_count())
							print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
							print("Torch version:", None)

try:
							import deepspeed

							print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
							print("DeepSpeed version:", None)

try:
							import tensorflow as tf

							print("TensorFlow version:", tf.__version__)
							print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
							print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
							print("TensorFlow version:", None)




