

import copy
import os
from typing import Union

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


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}

class       _SCREAMING_SNAKE_CASE	(__A ):
     lowerCAmelCase					=			"""git_vision_model"""





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							int=7_6_8     ,  UpperCamelCase :							Optional[Any]=3_0_7_2     ,  UpperCamelCase :							Dict=1_2     ,  UpperCamelCase :							Optional[int]=1_2     ,  UpperCamelCase :							int=3     ,  UpperCamelCase :							List[str]=2_2_4     ,  UpperCamelCase :							Optional[Any]=1_6     ,  UpperCamelCase :							List[Any]="quick_gelu"     ,  UpperCamelCase :							Union[str, Any]=1E-5     ,  UpperCamelCase :							int=0.0     ,  UpperCamelCase :							List[Any]=0.0_2     ,  **UpperCamelCase :							Optional[int]     ,  )->Optional[int]:
      super().__init__(**UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    List[str]       =	num_channels
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	patch_size
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_size
      __SCREAMING_SNAKE_CASE       :    int       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    int       =	attention_dropout
      __SCREAMING_SNAKE_CASE       :    int       =	layer_norm_eps
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hidden_act






     @classmethod
     def 	__snake_case    (       cls :							List[str]     ,  UpperCamelCase :							Any     ,  **UpperCamelCase :							List[str]				)->Optional[Any]:
      cls._set_token_in_kwargs(UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    Any       =	cls.get_config_dict(UpperCamelCase__     ,  **UpperCamelCase__				)

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get("model_type"				) == "git":
       __SCREAMING_SNAKE_CASE       :    Dict       =	config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls     ,  "model_type"				) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
           F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

      return cls.from_dict(UpperCamelCase__     ,  **UpperCamelCase__				)






class       _SCREAMING_SNAKE_CASE	(__A ):
     lowerCAmelCase					=			"""git"""





     def __init__(       self :							str     ,  UpperCamelCase :							Tuple=None     ,  UpperCamelCase :							List[str]=3_0_5_2_2     ,  UpperCamelCase :							Optional[int]=7_6_8     ,  UpperCamelCase :							Optional[Any]=6     ,  UpperCamelCase :							Tuple=1_2     ,  UpperCamelCase :							Optional[Any]=3_0_7_2     ,  UpperCamelCase :							List[Any]="gelu"     ,  UpperCamelCase :							Tuple=0.1     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							str=1_0_2_4     ,  UpperCamelCase :							Optional[Any]=0.0_2     ,  UpperCamelCase :							List[Any]=1E-12     ,  UpperCamelCase :							int=0     ,  UpperCamelCase :							Tuple="absolute"     ,  UpperCamelCase :							List[Any]=True     ,  UpperCamelCase :							Union[str, Any]=False     ,  UpperCamelCase :							Optional[Any]=1_0_1     ,  UpperCamelCase :							int=1_0_2     ,  UpperCamelCase :							Optional[int]=None     ,  **UpperCamelCase :							List[str]     ,  )->str:
      super().__init__(bos_token_id=UpperCamelCase__     ,  eos_token_id=UpperCamelCase__     ,  pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__				)

      if vision_config is None:
       __SCREAMING_SNAKE_CASE       :    Tuple       =	{}
       logger.info("vision_config is None. initializing the GitVisionConfig with default values."				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	GitVisionConfig(**UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    str       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    str       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max_position_embeddings
      __SCREAMING_SNAKE_CASE       :    Tuple       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    Any       =	layer_norm_eps
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	position_embedding_type
      __SCREAMING_SNAKE_CASE       :    str       =	use_cache
      __SCREAMING_SNAKE_CASE       :    Tuple       =	tie_word_embeddings
      __SCREAMING_SNAKE_CASE       :    str       =	num_image_with_embedding

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	bos_token_id
      __SCREAMING_SNAKE_CASE       :    Tuple       =	eos_token_id






     def 	__snake_case    (       self :							Optional[Any]				)->int:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	copy.deepcopy(self.__dict__				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.vision_config.to_dict()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.__class__.model_type
      return output











# 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 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	botoa.client("iam"     )

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
     "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=__lowerCamelCase		,	AssumeRolePolicyDocument=json.dumps(__lowerCamelCase		,	indent=2     )     )
  __SCREAMING_SNAKE_CASE       :    str       =	{
      "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=__lowerCamelCase		,	PolicyName=F"""{role_name}_policy_permission"""		,	PolicyDocument=json.dumps(__lowerCamelCase		,	indent=2     )		,	)
 except iam_client.exceptions.EntityAlreadyExistsException:
  print(F"""role {role_name} already exists. Using existing one"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[str]       =	botoa.client("iam"     )
 return iam_client.get_role(RoleName=__lowerCamelCase     )["Role"]["Arn"]






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	_ask_options(
     "How do you want to authorize?"		,	["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]		,	__lowerCamelCase		,	)
 __SCREAMING_SNAKE_CASE       :    str       =	None
 if credentials_configuration == 0:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	_ask_field("Enter your AWS Profile name: [default] "		,	default="default"     )
  __SCREAMING_SNAKE_CASE       :    Any       =	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`"     )
  __SCREAMING_SNAKE_CASE       :    Dict       =	_ask_field("AWS Access Key ID: "     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	aws_access_key_id

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	_ask_field("AWS Secret Access Key: "     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	aws_secret_access_key

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

 __SCREAMING_SNAKE_CASE       :    int       =	_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"]		,	__lowerCamelCase		,	)
 if role_management == 0:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	_ask_field("Enter your IAM role name: "     )
 else:
  __SCREAMING_SNAKE_CASE       :    Any       =	"accelerate_sagemaker_execution_role"
  print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""     )
  _create_iam_role_for_sagemaker(__lowerCamelCase     )

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

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

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

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

  if use_custom_options:
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	_ask_options(
       "Which mode do you want to use?"		,	__lowerCamelCase		,	lambda __lowerCamelCase     : TORCH_DYNAMO_MODES[int(__lowerCamelCase     )]		,	default="default"		,	)
   __SCREAMING_SNAKE_CASE       :    str       =	_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=__lowerCamelCase		,	error_message="Please enter yes or no."		,	)
   __SCREAMING_SNAKE_CASE       :    Any       =	_ask_field(
       "Do you want to enable dynamic shape tracing? [yes/NO]: "		,	_convert_yes_no_to_bool		,	default=__lowerCamelCase		,	error_message="Please enter yes or no."		,	)
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"Which EC2 instance type you want to use for your training?"
 if distributed_type != SageMakerDistributedType.NO:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	_ask_options(
      __lowerCamelCase		,	__lowerCamelCase		,	lambda __lowerCamelCase     : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__lowerCamelCase     )]     )
 else:
  eca_instance_query += "? [ml.p3.2xlarge]:"
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	_ask_field(__lowerCamelCase		,	lambda __lowerCamelCase     : str(__lowerCamelCase     ).lower()		,	default="ml.p3.2xlarge"     )

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

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	_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=__lowerCamelCase		,	compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER		,	distributed_type=__lowerCamelCase		,	use_cpu=__lowerCamelCase		,	dynamo_config=__lowerCamelCase		,	eca_instance_type=__lowerCamelCase		,	profile=__lowerCamelCase		,	region=__lowerCamelCase		,	iam_role_name=__lowerCamelCase		,	mixed_precision=__lowerCamelCase		,	num_machines=__lowerCamelCase		,	sagemaker_inputs_file=__lowerCamelCase		,	sagemaker_metrics_file=__lowerCamelCase		,	)






