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""": 650, """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""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]     )
class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					int      )					->				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 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Optional[Any]=1      )					->				Union[str, Any]:



												'''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=_UpperCamelCase	,							instance_type=self.instance_type	,							debugger_hook_config=_UpperCamelCase	,							hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}	,							metric_definitions=self.env.metric_definitions	,							py_version="py36"	,							)




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Optional[Any]      )					->				List[str]:



												'''simple docstring'''

												TrainingJobAnalytics(_UpperCamelCase      ).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"      )





							def 	__snake_case(						self   :					str      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.create_estimator()

												# run training
												estimator.fit()

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

												# extract kpis
												SCREAMING_SNAKE_CASE    			=       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]      )
												SCREAMING_SNAKE_CASE    			=       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    			=       (
												    Session().describe_training_job(estimator.latest_training_job.name      ).get("TrainingTimeInSeconds"	,							999_999      )
												)

												# 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      )





from typing import TYPE_CHECKING

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


_lowerCamelCase					:   Optional[int]							=		{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[Any]							=		['''BlenderbotTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Tuple							=		[
							    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BlenderbotForCausalLM''',
							    '''BlenderbotForConditionalGeneration''',
							    '''BlenderbotModel''',
							    '''BlenderbotPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   List[str]							=		[
							    '''TFBlenderbotForConditionalGeneration''',
							    '''TFBlenderbotModel''',
							    '''TFBlenderbotPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		[
							    '''FlaxBlenderbotForConditionalGeneration''',
							    '''FlaxBlenderbotModel''',
							    '''FlaxBlenderbotPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

							_lowerCamelCase					:   Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




