





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     =	{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	['''RoFormerTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoFormerForCausalLM''',
			    '''RoFormerForMaskedLM''',
			    '''RoFormerForMultipleChoice''',
			    '''RoFormerForQuestionAnswering''',
			    '''RoFormerForSequenceClassification''',
			    '''RoFormerForTokenClassification''',
			    '''RoFormerLayer''',
			    '''RoFormerModel''',
			    '''RoFormerPreTrainedModel''',
			    '''load_tf_weights_in_roformer''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRoFormerForCausalLM''',
			    '''TFRoFormerForMaskedLM''',
			    '''TFRoFormerForMultipleChoice''',
			    '''TFRoFormerForQuestionAnswering''',
			    '''TFRoFormerForSequenceClassification''',
			    '''TFRoFormerForTokenClassification''',
			    '''TFRoFormerLayer''',
			    '''TFRoFormerModel''',
			    '''TFRoFormerPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxRoFormerForMaskedLM''',
			    '''FlaxRoFormerForMultipleChoice''',
			    '''FlaxRoFormerForQuestionAnswering''',
			    '''FlaxRoFormerForSequenceClassification''',
			    '''FlaxRoFormerForTokenClassification''',
			    '''FlaxRoFormerModel''',
			    '''FlaxRoFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""simple docstring"""





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

import pytest
from parameterized import 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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""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=_A      ,    )
											assert hasattr(self      ,    """env""")





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											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}-single"""      ,    instance_count=_A      ,    instance_type=self.instance_type      ,    debugger_hook_config=_A      ,    hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,    metric_definitions=self.env.metric_definitions      ,    py_version="""py36"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											_SCREAMING_SNAKE_CASE		:						Any			  =    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

											# extract kpis
											_SCREAMING_SNAKE_CASE		:						Any			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											_SCREAMING_SNAKE_CASE		:						int			  =    (
											    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}      ,    _A)


