
"""simple docstring"""

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
   from sagemaker import Session, TrainingJobAnalytics
   from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"  ,      "False"				)				) is not True  ,      reason="Skipping test because should only be run when releasing minor transformers version"  ,      )
@pytest.mark.usefixtures("sm_env"				)
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       Optional[int]:





          if self.framework == "pytorch":
              subprocess.run(
                  F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,							encoding='utf-8' ,							check=lowercase_ ,							)
          assert hasattr(self ,							'env'				)




      def      a_   (				self ,							lowercase_				)    ->       int:
          UpperCAmelCase          =       {
              "enabled": True,
              "processes_per_host": 8,
          }
          UpperCAmelCase          =       {
              "enabled": True,
              "parameters": {
                  "microbatches": 4,
                  "placement_strategy": "spread",
                  "pipeline": "interleaved",
                  "optimize": "speed",
                  "partitions": 4,
                  "ddp": True,
              },
          }

          UpperCAmelCase          =       {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

          UpperCAmelCase          =       "trainer" if self.script == "run_glue.py" else "smtrainer"
          # creates estimator
          return HuggingFace(
              entry_point=self.script ,							source_dir=self.env.test_path ,							role=self.env.role ,							image_uri=self.env.image_uri ,							base_job_name=F"{self.env.base_job_name}-{instance_count}-smp-{name_extension}" ,							instance_count=lowercase_ ,							instance_type=self.instance_type ,							debugger_hook_config=lowercase_ ,							hyperparameters={
                  **self.env.hyperparameters,
                  'model_name_or_path': self.model_name_or_path,
                  'max_steps': 5_0_0,
              } ,							metric_definitions=self.env.metric_definitions ,							distribution=lowercase_ ,							py_version='py36' ,							)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          TrainingJobAnalytics(lowercase_				).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"				)




      @parameterized.expand([(1,)]				)
      def      a_   (				self ,							lowercase_				)    ->       str:
          UpperCAmelCase          =       self.create_estimator(lowercase_				)

          # run training
          estimator.fit()

          # result dataframe
          UpperCAmelCase          =       TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

          # extract kpis
          UpperCAmelCase          =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']				)
          UpperCAmelCase          =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']				)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          UpperCAmelCase          =       (
              Session().describe_training_job(estimator.latest_training_job.name				).get('TrainingTimeInSeconds' ,							9_9_9_9_9_9				)
          )

          # assert kpis
          assert train_runtime <= self.results["train_runtime"]
          assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy				)
          assert all(t <= self.results['eval_loss'] for t in eval_loss				)

          # dump tests result into json file to share in PR
          with open(F"{estimator.latest_training_job.name}.json" ,							'w'				) as outfile:
              json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} ,							lowercase_				)







'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser(
          description=(
              """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
              """ Distillation"""
          )
      )
      parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
      parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
      parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
      parser.add_argument("""--vocab_transform""", action="""store_true""")
      _UpperCamelCase 							=  parser.parse_args()

      if args.model_type == "roberta":
            _UpperCamelCase 							=  RobertaForMaskedLM.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """roberta"""
      elif args.model_type == "gpt2":
            _UpperCamelCase 							=  GPTaLMHeadModel.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """transformer"""

      _UpperCamelCase 							=  model.state_dict()
      _UpperCamelCase 							=  {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.{param_name}"""]
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.{w}.weight"""
                  _UpperCamelCase 							=  state_dict[param_name]
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.LayerNorm.{w}"""
                  _UpperCamelCase 							=  state_dict[param_name]

    # Transformer Blocks #
      _UpperCamelCase 							=  0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _UpperCamelCase 							=  state_dict[F"""lm_head.dense.{w}"""]
                        _UpperCamelCase 							=  state_dict[F"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.ln_f.{w}"""]
            _UpperCamelCase 							=  state_dict["""lm_head.weight"""]

      print(F"""N layers selected for distillation: {std_idx}""")
      print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

      print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
      torch.save(compressed_sd, args.dump_checkpoint)

