



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[torch.FloatTensor]         =					None
							UpperCamelCase__				:      torch.FloatTensor         =					None
							UpperCamelCase__				:      Optional[Tuple[torch.FloatTensor]]         =					None
							UpperCamelCase__				:      Optional[Tuple[torch.FloatTensor]]         =					None




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  a_=1  ,  a_=0  ,  a_=2  ,  a_=512  ,  a_="cls"  ,  a_=False  ,  a_=True  ,  **a_  ,  ):
											super().__init__(pad_token_id=a_  ,  bos_token_id=a_  ,  eos_token_id=a_  ,  **a_		)
											a__				      =		project_dim
											a__				      =		pooler_fn
											a__				      =		learn_encoder
											a__				      =		use_attention_mask




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					[r"""pooler""", r"""logit_scale"""]
							UpperCamelCase__				:      List[Any]         =					[r"""position_ids""", r"""predictions.decoder.bias"""]
							UpperCamelCase__				:      List[Any]         =					"""roberta"""
							UpperCamelCase__				:      int         =					RobertaSeriesConfig


							def __init__(							self  ,  a_		):
											super().__init__(a_		)
											a__				      =		XLMRobertaModel(a_		)
											a__				      =		nn.Linear(config.hidden_size  ,  config.project_dim		)
											a__				      =		getattr(a_  ,  """has_pre_transformation"""  ,  a_		)
											if self.has_pre_transformation:
															a__				      =		nn.Linear(config.hidden_size  ,  config.project_dim		)
															a__				      =		nn.LayerNorm(config.hidden_size  ,  eps=config.layer_norm_eps		)
											self.post_init()







							def _a      (							self  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  ):
											a__				      =		return_dict if return_dict is not None else self.config.use_return_dict

											a__				      =		self.base_model(
											    input_ids=a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  position_ids=a_  ,  head_mask=a_  ,  inputs_embeds=a_  ,  encoder_hidden_states=a_  ,  encoder_attention_mask=a_  ,  output_attentions=a_  ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states  ,  return_dict=a_  ,  )

											if self.has_pre_transformation:
															a__				      =		outputs["""hidden_states"""][-2]
															a__				      =		self.pre_LN(a_		)
															a__				      =		self.transformation_pre(a_		)

															return TransformationModelOutput(
															    projection_state=a_  ,  last_hidden_state=outputs.last_hidden_state  ,  hidden_states=outputs.hidden_states  ,  attentions=outputs.attentions  ,  )
											else:
															a__				      =		self.transformation(outputs.last_hidden_state		)
															return TransformationModelOutput(
															    projection_state=a_  ,  last_hidden_state=outputs.last_hidden_state  ,  hidden_states=outputs.hidden_states  ,  attentions=outputs.attentions  ,  )






# 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							 =					"""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)

