
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        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Any							 =       [R"""pooler""", R"""logit_scale"""]
	_UpperCamelCase     :     Dict							 =       [R"""position_ids""", R"""predictions.decoder.bias"""]
	_UpperCamelCase     :     int							 =       """roberta"""
	_UpperCamelCase     :     Any							 =       RobertaSeriesConfig





	def __init__(       self		,    snake_case       ):
		super().__init__(snake_case       )
		lowercase       =    XLMRobertaModel(snake_case       )
		lowercase       =    nn.Linear(config.hidden_size		,    config.project_dim       )
		lowercase       =    getattr(snake_case		,    'has_pre_transformation'		,    snake_case       )
		if self.has_pre_transformation:
			lowercase       =    nn.Linear(config.hidden_size		,    config.project_dim       )
			lowercase       =    nn.LayerNorm(config.hidden_size		,    eps=config.layer_norm_eps       )
		self.post_init()





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    ):
		lowercase       =    return_dict if return_dict is not None else self.config.use_return_dict

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

		if self.has_pre_transformation:
			lowercase       =    outputs['hidden_states'][-2]
			lowercase       =    self.pre_LN(snake_case       )
			lowercase       =    self.transformation_pre(snake_case       )

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



# Copyright 2021 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase	      = '''Create a default config file for Accelerate with only a few flags set.'''






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE="no" ,							__SCREAMING_SNAKE_CASE = default_json_config_file ,							__SCREAMING_SNAKE_CASE = False							):
	lowercase       =    Path(__SCREAMING_SNAKE_CASE							)
	path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if path.exists():
		print(
		    F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''							)
		return False
	lowercase       =    mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''							)
	lowercase       =    {
	    'compute_environment': 'LOCAL_MACHINE',
	    'mixed_precision': mixed_precision,
	}
	if torch.cuda.is_available():
		lowercase       =    torch.cuda.device_count()
		lowercase       =    num_gpus
		lowercase       =    False
		if num_gpus > 1:
			lowercase       =    'MULTI_GPU'
		else:
			lowercase       =    'NO'
	elif is_xpu_available() and use_xpu:
		lowercase       =    torch.xpu.device_count()
		lowercase       =    num_xpus
		lowercase       =    False
		if num_xpus > 1:
			lowercase       =    'MULTI_XPU'
		else:
			lowercase       =    'NO'
	elif is_npu_available():
		lowercase       =    torch.npu.device_count()
		lowercase       =    num_npus
		lowercase       =    False
		if num_npus > 1:
			lowercase       =    'MULTI_NPU'
		else:
			lowercase       =    'NO'
	else:
		lowercase       =    0
		lowercase       =    True
		lowercase       =    1
		lowercase       =    'NO'
	lowercase       =    ClusterConfig(**__SCREAMING_SNAKE_CASE							)
	config.to_json_file(__SCREAMING_SNAKE_CASE							)
	return path






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    parser.add_parser('default' ,							parents=__SCREAMING_SNAKE_CASE ,							help=__SCREAMING_SNAKE_CASE ,							formatter_class=__SCREAMING_SNAKE_CASE							)
	parser.add_argument(
	    '--config_file' ,							default=__SCREAMING_SNAKE_CASE ,							help=(
	        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
	        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
	        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
	        'with \'huggingface\'.'
	    ) ,							dest='save_location' ,							)

	parser.add_argument(
	    '--mixed_precision' ,							choices=['no', 'fp16', 'bf16'] ,							type=__SCREAMING_SNAKE_CASE ,							help='Whether or not to use mixed precision training. '
	    'Choose between FP16 and BF16 (bfloat16) training. '
	    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' ,							default='no' ,							)
	parser.set_defaults(func=__SCREAMING_SNAKE_CASE							)
	return parser






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    write_basic_config(args.mixed_precision ,							args.save_location							)
	if config_file:
		print(F'''accelerate configuration saved at {config_file}'''							)

