


"""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 _lowerCAmelCase				(      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=UpperCamelCase__						,	)
       assert hasattr(self						,	"env"						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[str]      					=    {
           "enabled": True,
           "processes_per_host": 8,
       }
       snake_case    :      Optional[int]      					=    {
           "enabled": True,
           "parameters": {
               "microbatches": 4,
               "placement_strategy": "spread",
               "pipeline": "interleaved",
               "optimize": "speed",
               "partitions": 4,
               "ddp": True,
           },
       }

       snake_case    :      int      					=    {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

       snake_case    :      Dict      					=    "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=UpperCamelCase__						,	instance_type=self.instance_type						,	debugger_hook_config=UpperCamelCase__						,	hyperparameters={
               **self.env.hyperparameters,
               "model_name_or_path": self.model_name_or_path,
               "max_steps": 500,
           }						,	metric_definitions=self.env.metric_definitions						,	distribution=UpperCamelCase__						,	py_version="py36"						,	)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       TrainingJobAnalytics(UpperCamelCase__						).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'						)







   @parameterized.expand([(1,)]						)
   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Any      					=    self.create_estimator(UpperCamelCase__						)

       # run training
       estimator.fit()

       # result dataframe
       snake_case    :      Tuple      					=    TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       snake_case    :      Any      					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
       snake_case    :      Union[str, Any]      					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       snake_case    :      Tuple      					=    (
           Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"						,	99_9999						)
       )

       # 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}						,	UpperCamelCase__						)









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  '''xmod'''







   def __init__(	self						,	UpperCamelCase__=3_0522						,	UpperCamelCase__=768						,	UpperCamelCase__=12						,	UpperCamelCase__=12						,	UpperCamelCase__=3072						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=1e-12						,	UpperCamelCase__=1						,	UpperCamelCase__=0						,	UpperCamelCase__=2						,	UpperCamelCase__="absolute"						,	UpperCamelCase__=True						,	UpperCamelCase__=None						,	UpperCamelCase__=False						,	UpperCamelCase__=2						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=("en_XX",)						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		int:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,	bos_token_id=UpperCamelCase__						,	eos_token_id=UpperCamelCase__						,	**UpperCamelCase__						)

       snake_case    :      List[Any]      					=    vocab_size
       snake_case    :      List[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Any      					=    num_attention_heads
       snake_case    :      List[str]      					=    hidden_act
       snake_case    :      Union[str, Any]      					=    intermediate_size
       snake_case    :      int      					=    hidden_dropout_prob
       snake_case    :      Any      					=    attention_probs_dropout_prob
       snake_case    :      Optional[int]      					=    max_position_embeddings
       snake_case    :      Tuple      					=    type_vocab_size
       snake_case    :      List[str]      					=    initializer_range
       snake_case    :      int      					=    layer_norm_eps
       snake_case    :      Optional[Any]      					=    position_embedding_type
       snake_case    :      int      					=    use_cache
       snake_case    :      Dict      					=    classifier_dropout
       snake_case    :      Dict      					=    pre_norm
       snake_case    :      Union[str, Any]      					=    adapter_reduction_factor
       snake_case    :      Any      					=    adapter_layer_norm
       snake_case    :      Optional[int]      					=    adapter_reuse_layer_norm
       snake_case    :      List[Any]      					=    ln_before_adapter
       snake_case    :      str      					=    list(UpperCamelCase__						)
       snake_case    :      int      					=    default_language







class _lowerCAmelCase				(      snake_case_	):







   @property
   def  lowerCamelCase		(	self						)	->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case    :      Optional[int]      					=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case    :      Optional[Any]      					=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]						)





