"""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""": 1600, """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""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]    )
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        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   lowerCamelCase_       (						self   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     {
            'enabled': True,
            'processes_per_host': 8,
        }
        a__:					Union[str, Any]      							=     {
            'enabled': True,
            'parameters': {
                'microbatches': 4,
                'placement_strategy': 'spread',
                'pipeline': 'interleaved',
                'optimize': 'speed',
                'partitions': 4,
                'ddp': True,
            },
        }

        a__:					List[Any]      							=     {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

        a__:					List[str]      							=     '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_00,
            }   ,      metric_definitions=self.env.metric_definitions   ,      distribution=lowercase   ,      py_version='py36'   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        TrainingJobAnalytics(lowercase).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv')



      @parameterized.expand([(1,)])
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					str      							=     self.create_estimator(lowercase)

        # run training
        estimator.fit()

        # result dataframe
        a__:					List[Any]      							=     TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

        # extract kpis
        a__:					Union[str, Any]      							=     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
        a__:					int      							=     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        a__:					Union[str, Any]      							=     (
            Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'   ,      99_99_99)
        )

        # 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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase__ 	=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': 512,
}

lowercase__ 	=     {
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		PRETRAINED_INIT_CONFIGURATION
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		LxmertTokenizer



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase=True   ,      lowercase="[UNK]"   ,      lowercase="[SEP]"   ,      lowercase="[PAD]"   ,      lowercase="[CLS]"   ,      lowercase="[MASK]"   ,      lowercase=True   ,      lowercase=None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





        super().__init__(
            lowercase   ,      tokenizer_file=lowercase   ,      do_lower_case=lowercase   ,      unk_token=lowercase   ,      sep_token=lowercase   ,      pad_token=lowercase   ,      cls_token=lowercase   ,      mask_token=lowercase   ,      tokenize_chinese_chars=lowercase   ,      strip_accents=lowercase   ,      **lowercase   ,      )

        a__:					Dict      							=     json.loads(self.backend_tokenizer.normalizer.__getstate__())
        if (
            normalizer_state.get('lowercase'   ,      lowercase) != do_lower_case
            or normalizer_state.get('strip_accents'   ,      lowercase) != strip_accents
            or normalizer_state.get('handle_chinese_chars'   ,      lowercase) != tokenize_chinese_chars
        ):
          a__:					int      							=     getattr(lowercase   ,      normalizer_state.pop('type'))
          a__:					Dict      							=     do_lower_case
          a__:					Dict      							=     strip_accents
          a__:					Optional[int]      							=     tokenize_chinese_chars
          a__:					List[Any]      							=     normalizer_class(**lowercase)

        a__:					Optional[int]      							=     do_lower_case



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[Any]      							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

        return output



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     [self.sep_token_id]
        a__:					List[Any]      							=     [self.cls_token_id]
        if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
        return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        a__:					List[Any]      							=     self._tokenizer.model.save(lowercase   ,      name=lowercase)
        return tuple(lowercase)


