


'''simple docstring'''
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Dict									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				int									=		{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Dict   						="wav2vec2"






						def __init__(  self							:      str ,  lowerCAmelCase							:      Dict=32 ,  lowerCAmelCase							:      List[Any]=7_68 ,  lowerCAmelCase							:      List[str]=12 ,  lowerCAmelCase							:      List[str]=12 ,  lowerCAmelCase							:      List[Any]=30_72 ,  lowerCAmelCase							:      Optional[Any]="gelu" ,  lowerCAmelCase							:      Optional[int]=0.1 ,  lowerCAmelCase							:      Optional[int]=0.1 ,  lowerCAmelCase							:      List[Any]=0.1 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      Union[str, Any]=0.0 ,  lowerCAmelCase							:      str=0.1 ,  lowerCAmelCase							:      List[str]=0.1 ,  lowerCAmelCase							:      Dict=0.0_2 ,  lowerCAmelCase							:      List[str]=1e-5 ,  lowerCAmelCase							:      Optional[Any]="group" ,  lowerCAmelCase							:      List[Any]="gelu" ,  lowerCAmelCase							:      int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,  lowerCAmelCase							:      Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) ,  lowerCAmelCase							:      Any=(10, 3, 3, 3, 3, 2, 2) ,  lowerCAmelCase							:      str=False ,  lowerCAmelCase							:      Tuple=1_28 ,  lowerCAmelCase							:      Union[str, Any]=16 ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      Union[str, Any]=True ,  lowerCAmelCase							:      Any=0.0_5 ,  lowerCAmelCase							:      Any=10 ,  lowerCAmelCase							:      Dict=2 ,  lowerCAmelCase							:      List[Any]=0.0 ,  lowerCAmelCase							:      int=10 ,  lowerCAmelCase							:      List[str]=0 ,  lowerCAmelCase							:      Tuple=3_20 ,  lowerCAmelCase							:      List[str]=2 ,  lowerCAmelCase							:      int=0.1 ,  lowerCAmelCase							:      List[Any]=1_00 ,  lowerCAmelCase							:      List[str]=2_56 ,  lowerCAmelCase							:      int=2_56 ,  lowerCAmelCase							:      Optional[Any]=0.1 ,  lowerCAmelCase							:      str="sum" ,  lowerCAmelCase							:      List[Any]=False ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      Tuple=2_56 ,  lowerCAmelCase							:      str=(5_12, 5_12, 5_12, 5_12, 15_00) ,  lowerCAmelCase							:      List[str]=(5, 3, 3, 1, 1) ,  lowerCAmelCase							:      Tuple=(1, 2, 3, 1, 1) ,  lowerCAmelCase							:      Optional[Any]=5_12 ,  lowerCAmelCase							:      Any=0 ,  lowerCAmelCase							:      str=1 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      Any=False ,  lowerCAmelCase							:      List[Any]=3 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      List[str]=3 ,  lowerCAmelCase							:      int=None ,  lowerCAmelCase							:      Optional[int]=None ,  **lowerCAmelCase							:      Union[str, Any] ,  ):
													super().__init__(**lowerCAmelCase ,  pad_token_id=lowerCAmelCase ,  bos_token_id=lowerCAmelCase ,  eos_token_id=lowerCAmelCase	)
													A_			    =		hidden_size
													A_			    =		feat_extract_norm
													A_			    =		feat_extract_activation
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		conv_bias
													A_			    =		num_conv_pos_embeddings
													A_			    =		num_conv_pos_embedding_groups
													A_			    =		len(self.conv_dim	)
													A_			    =		num_hidden_layers
													A_			    =		intermediate_size
													A_			    =		hidden_act
													A_			    =		num_attention_heads
													A_			    =		hidden_dropout
													A_			    =		attention_dropout
													A_			    =		activation_dropout
													A_			    =		feat_proj_dropout
													A_			    =		final_dropout
													A_			    =		layerdrop
													A_			    =		layer_norm_eps
													A_			    =		initializer_range
													A_			    =		vocab_size
													A_			    =		do_stable_layer_norm
													A_			    =		use_weighted_layer_sum

													if (
													    (len(self.conv_stride	) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
													    or (len(self.conv_dim	) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																				    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																				    F" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"
																				    F" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													A_			    =		apply_spec_augment
													A_			    =		mask_time_prob
													A_			    =		mask_time_length
													A_			    =		mask_time_min_masks
													A_			    =		mask_feature_prob
													A_			    =		mask_feature_length
													A_			    =		mask_feature_min_masks

													# parameters for pretraining with codevector quantized representations
													A_			    =		num_codevectors_per_group
													A_			    =		num_codevector_groups
													A_			    =		contrastive_logits_temperature
													A_			    =		feat_quantizer_dropout
													A_			    =		num_negatives
													A_			    =		codevector_dim
													A_			    =		proj_codevector_dim
													A_			    =		diversity_loss_weight

													# ctc loss
													A_			    =		ctc_loss_reduction
													A_			    =		ctc_zero_infinity

													# adapter
													A_			    =		add_adapter
													A_			    =		adapter_kernel_size
													A_			    =		adapter_stride
													A_			    =		num_adapter_layers
													A_			    =		output_hidden_size or hidden_size
													A_			    =		adapter_attn_dim

													# SequenceClassification-specific parameter. Feel free to ignore for other classes.
													A_			    =		classifier_proj_size

													# XVector-specific parameters. Feel free to ignore for other classes.
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		xvector_output_dim






						@property
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													return functools.reduce(operator.mul ,  self.conv_stride ,  1	)









'''simple docstring'''
# 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.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
							import botoa  # noqa: F401







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )

							A_			    =		{
							    "Version": "2012-10-17",
							    "Statement": [
							        {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
							    ],
							}
							try:
														# create the role, associated with the chosen trust policy
														iam_client.create_role(
														    RoleName=UpperCamelCase_						,  AssumeRolePolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 ) )
														A_			    =		{
														    "Version": "2012-10-17",
														    "Statement": [
														        {
														            "Effect": "Allow",
														            "Action": [
														                "sagemaker:*",
														                "ecr:GetDownloadUrlForLayer",
														                "ecr:BatchGetImage",
														                "ecr:BatchCheckLayerAvailability",
														                "ecr:GetAuthorizationToken",
														                "cloudwatch:PutMetricData",
														                "cloudwatch:GetMetricData",
														                "cloudwatch:GetMetricStatistics",
														                "cloudwatch:ListMetrics",
														                "logs:CreateLogGroup",
														                "logs:CreateLogStream",
														                "logs:DescribeLogStreams",
														                "logs:PutLogEvents",
														                "logs:GetLogEvents",
														                "s3:CreateBucket",
														                "s3:ListBucket",
														                "s3:GetBucketLocation",
														                "s3:GetObject",
														                "s3:PutObject",
														            ],
														            "Resource": "*",
														        }
														    ],
														}
														# attach policy to role
														iam_client.put_role_policy(
														    RoleName=UpperCamelCase_						,  PolicyName=f"{role_name}_policy_permission"						,  PolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 )						,  )
							except iam_client.exceptions.EntityAlreadyExistsException:
														print(f"role {role_name} already exists. Using existing one" )







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )
							return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]







def       a_				(	):
							A_			    =		_ask_options(
							    "How do you want to authorize?"						,  ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,  UpperCamelCase_						,  )
							A_			    =		None
							if credentials_configuration == 0:
														A_			    =		_ask_field("Enter your AWS Profile name: [default] "						,  default="default" )
														A_			    =		aws_profile
							else:
														print(
														    "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
														    "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
														A_			    =		_ask_field("AWS Access Key ID: " )
														A_			    =		aws_access_key_id

														A_			    =		_ask_field("AWS Secret Access Key: " )
														A_			    =		aws_secret_access_key

							A_			    =		_ask_field("Enter your AWS Region: [us-east-1]"						,  default="us-east-1" )
							A_			    =		aws_region

							A_			    =		_ask_options(
							    "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"						,  ["Provide IAM Role name", "Create new IAM role using credentials"]						,  UpperCamelCase_						,  )
							if role_management == 0:
														A_			    =		_ask_field("Enter your IAM role name: " )
							else:
														A_			    =		"accelerate_sagemaker_execution_role"
														print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
														_create_iam_role_for_sagemaker(UpperCamelCase_ )

							A_			    =		_ask_field(
							    "Do you want to use custom Docker image? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_custom_docker_image:
														A_			    =		_ask_field("Enter your Docker image: "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )

							A_			    =		_ask_field(
							    "Do you want to provide SageMaker input channels with data locations? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_inputs_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_field(
							    "Do you want to enable SageMaker metrics? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_metrics_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_options(
							    "What is the distributed mode?"						,  ["No distributed training", "Data parallelism"]						,  _convert_sagemaker_distributed_mode						,  )
							A_			    =		{}
							A_			    =		_ask_field(
							    "Do you wish to optimize your script with torch dynamo?[yes/NO]:"						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							if use_dynamo:
														A_			    =		"dynamo_"
														A_			    =		_ask_options(
														    "Which dynamo backend would you like to use?"						,  [x.lower() for x in DYNAMO_BACKENDS]						,  _convert_dynamo_backend						,  default=2						,  )
														A_			    =		_ask_field(
														    "Do you want to customize the defaults sent to torch.compile? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )

														if use_custom_options:
																					A_			    =		_ask_options(
																					    "Which mode do you want to use?"						,  UpperCamelCase_						,  lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )]						,  default="default"						,  )
																					A_			    =		_ask_field(
																					    "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
																					A_			    =		_ask_field(
																					    "Do you want to enable dynamic shape tracing? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		"Which EC2 instance type you want to use for your training?"
							if distributed_type != SageMakerDistributedType.NO:
														A_			    =		_ask_options(
														    UpperCamelCase_						,  UpperCamelCase_						,  lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
							else:
														eca_instance_query += "? [ml.p3.2xlarge]:"
														A_			    =		_ask_field(UpperCamelCase_						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  default="ml.p3.2xlarge" )

							A_			    =		1
							if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
														A_			    =		_ask_field(
														    "How many machines do you want use? [1]: "						,  UpperCamelCase_						,  default=1						,  )

							A_			    =		_ask_options(
							    "Do you wish to use FP16 or BF16 (mixed precision)?"						,  ["no", "fp16", "bf16", "fp8"]						,  _convert_mixed_precision						,  )

							if use_dynamo and mixed_precision == "no":
														print(
														    "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )

							return SageMakerConfig(
							    image_uri=UpperCamelCase_						,  compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER						,  distributed_type=UpperCamelCase_						,  use_cpu=UpperCamelCase_						,  dynamo_config=UpperCamelCase_						,  eca_instance_type=UpperCamelCase_						,  profile=UpperCamelCase_						,  region=UpperCamelCase_						,  iam_role_name=UpperCamelCase_						,  mixed_precision=UpperCamelCase_						,  num_machines=UpperCamelCase_						,  sagemaker_inputs_file=UpperCamelCase_						,  sagemaker_metrics_file=UpperCamelCase_						,  )





