





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     ["image_processor", "tokenizer"]
 lowercase_						=     "ChineseCLIPImageProcessor"
 lowercase_						=     ("BertTokenizer", "BertTokenizerFast")

 def __init__(self							:  Any			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  str)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: str         =None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."			,					UpperCAmelCase_			,					)
     lowerCamelCase__: Tuple         =kwargs.pop("feature_extractor")

   lowerCamelCase__: Optional[int]         =image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(UpperCAmelCase_			,					UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =self.image_processor

 def __call__(self							:  int			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					**UpperCAmelCase_							:  Dict)			->Optional[int]:



   '''simple docstring'''



   if text is None and images is None:
     raise ValueError("You have to specify either text or images. Both cannot be none.")

   if text is not None:
     lowerCamelCase__: Dict         =self.tokenizer(UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					**UpperCAmelCase_)

   if images is not None:
     lowerCamelCase__: List[str]         =self.image_processor(UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					**UpperCAmelCase_)

   if text is not None and images is not None:
     lowerCamelCase__: Union[str, Any]         =image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**UpperCAmelCase_)			,					tensor_type=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					*UpperCAmelCase_							:  Dict			,					**UpperCAmelCase_							:  int)			->str:



   '''simple docstring'''



   return self.tokenizer.batch_decode(*UpperCAmelCase_			,					**UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					*UpperCAmelCase_							:  Optional[Any]			,					**UpperCAmelCase_							:  List[Any])			->Dict:



   '''simple docstring'''



   return self.tokenizer.decode(*UpperCAmelCase_			,					**UpperCAmelCase_)

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.tokenizer.model_input_names
   lowerCamelCase__: Union[str, Any]         =self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->str:



   '''simple docstring'''



   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,					UpperCAmelCase_			,					)
   return self.image_processor_class





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      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''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=SCREAMING_SNAKE_CASE  ,     )
											assert hasattr(self  ,     "env"    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Any=1    )      ->						List[str]:




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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						List[str]:




											'''simple docstring'''

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






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.create_estimator()

											# run training
											estimator.fit()

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

											# extract kpis
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
											__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
											__snake_case				=		(
											    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}  ,     SCREAMING_SNAKE_CASE    )





