






# 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       lowercase__	(					A_:						Union[str, Any]		)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=botoa.client("""iam"""		)

       __UpperCAmelCase     				={
           """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=A_  ,      AssumeRolePolicyDocument=json.dumps(A_  ,      indent=2		)		)
              __UpperCAmelCase     				={
                  """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=A_  ,      PolicyName=F'''{role_name}_policy_permission'''  ,      PolicyDocument=json.dumps(A_  ,      indent=2		)  ,      )
       except iam_client.exceptions.EntityAlreadyExistsException:
              print(F'''role {role_name} already exists. Using existing one'''		)







def       lowercase__	(					A_:						Dict		)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				=botoa.client("""iam"""		)
       return iam_client.get_role(RoleName=A_		)["Role"]["Arn"]







def       lowercase__	(					)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=_ask_options(
           """How do you want to authorize?"""  ,      ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]  ,      A_  ,      )
       __UpperCAmelCase     				=None
       if credentials_configuration == 0:
              __UpperCAmelCase     				=_ask_field("""Enter your AWS Profile name: [default] """  ,      default="""default"""		)
              __UpperCAmelCase     				=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`"""		)
              __UpperCAmelCase     				=_ask_field("""AWS Access Key ID: """		)
              __UpperCAmelCase     				=aws_access_key_id

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

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

       __UpperCAmelCase     				=_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"""]  ,      A_  ,      )
       if role_management == 0:
              __UpperCAmelCase     				=_ask_field("""Enter your IAM role name: """		)
       else:
              __UpperCAmelCase     				="""accelerate_sagemaker_execution_role"""
              print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials'''		)
              _create_iam_role_for_sagemaker(A_		)

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

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

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

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

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

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

       __UpperCAmelCase     				=_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=A_  ,      compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER  ,      distributed_type=A_  ,      use_cpu=A_  ,      dynamo_config=A_  ,      eca_instance_type=A_  ,      profile=A_  ,      region=A_  ,      iam_role_name=A_  ,      mixed_precision=A_  ,      num_machines=A_  ,      sagemaker_inputs_file=A_  ,      sagemaker_metrics_file=A_  ,      )












import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
  raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    roberta.eval()  # disable dropout
    __A       =						roberta.model.encoder.sentence_encoder
    __A       =						XLMRobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,					hidden_size=roberta.cfg.model.encoder_embed_dim      ,					num_hidden_layers=roberta.cfg.model.encoder_layers      ,					num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,					max_position_embeddings=5_1_4      ,					type_vocab_size=1      ,					layer_norm_eps=1E-5      ,					)
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

    print("""Our RoBERTa config:"""      ,					__lowercase     )

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    __A       =						roberta_sent_encoder.embed_tokens.weight
    __A       =						roberta_sent_encoder.embed_positions.weight
    __A       =						torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

    __A       =						roberta_sent_encoder.layer_norm.weight
    __A       =						roberta_sent_encoder.layer_norm.bias

    for i in range(config.num_hidden_layers     ):
        # Encoder: start of layer
        __A       =						model.roberta.encoder.layer[i]
        __A       =						roberta_sent_encoder.layers[i]

        __A       =						layer.attention
        __A       =						roberta_layer.self_attn_layer_norm.weight
        __A       =						roberta_layer.self_attn_layer_norm.bias

        # self attention
        __A       =						layer.attention.self
        assert (
            roberta_layer.self_attn.k_proj.weight.data.shape
            == roberta_layer.self_attn.q_proj.weight.data.shape
            == roberta_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)     )
        )

        __A       =						roberta_layer.self_attn.q_proj.weight
        __A       =						roberta_layer.self_attn.q_proj.bias
        __A       =						roberta_layer.self_attn.k_proj.weight
        __A       =						roberta_layer.self_attn.k_proj.bias
        __A       =						roberta_layer.self_attn.v_proj.weight
        __A       =						roberta_layer.self_attn.v_proj.bias

        # self-attention output
        __A       =						layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        __A       =						roberta_layer.self_attn.out_proj.weight
        __A       =						roberta_layer.self_attn.out_proj.bias

        # this one is final layer norm
        __A       =						roberta_layer.final_layer_norm.weight
        __A       =						roberta_layer.final_layer_norm.bias

        # intermediate
        __A       =						layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias

        # output
        __A       =						layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        __A       =						roberta_layer.fca.weight
        __A       =						roberta_layer.fca.bias
        # end of layer

    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].dense.weight
        __A       =						roberta.model.classification_heads["""mnli"""].dense.bias
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.bias
    else:
        # LM Head
        __A       =						roberta.model.encoder.lm_head.dense.weight
        __A       =						roberta.model.encoder.lm_head.dense.bias
        __A       =						roberta.model.encoder.lm_head.layer_norm.weight
        __A       =						roberta.model.encoder.lm_head.layer_norm.bias
        __A       =						roberta.model.encoder.lm_head.weight
        __A       =						roberta.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    __A       =						roberta.encode(__lowercase     ).unsqueeze(0     )  # batch of size 1

    __A       =						model(__lowercase     )[0]
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""](roberta.extract_features(__lowercase     )     )
    else:
        __A       =						roberta.model(__lowercase     )[0]
    print(our_output.shape      ,					their_output.shape     )
    __A       =						torch.max(torch.abs(our_output - their_output     )     ).item()
    print(f"max_absolute_diff = {max_absolute_diff}"     )  # ~ 1e-7
    __A       =						torch.allclose(__lowercase      ,					__lowercase      ,					atol=1E-3     )
    print("""Do both models output the same tensors?"""      ,					"""🔥""" if success else """💩"""     )
    if not success:
        raise Exception("""Something went wRoNg"""     )

    pathlib.Path(__lowercase     ).mkdir(parents=__lowercase      ,					exist_ok=__lowercase     )
    print(f"Saving model to {pytorch_dump_folder_path}"     )
    model.save_pretrained(__lowercase     )


if __name__ == "__main__":
  __a   :							List[Any]						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--classification_head", action="store_true", help="Whether to convert a final classification head."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )




