





import warnings

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



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
							_UpperCamelCase				:					Optional[int]         =   ['''image_processor''', '''tokenizer''']
							_UpperCamelCase				:					Union[str, Any]         =   '''ViTImageProcessor'''
							_UpperCamelCase				:					List[str]         =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

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




														"""simple docstring"""


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

														UpperCamelCase_          =  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__(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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




														"""simple docstring"""


														if text is None and visual_prompt is None and images is None:
																					raise ValueError("You have to specify either text, visual prompt or images."		)

														if text is not None and visual_prompt is not None:
																					raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."		)

														if text is not None:
																					UpperCamelCase_          =  self.tokenizer(_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

														if visual_prompt is not None:
																					UpperCamelCase_          =  self.image_processor(_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

														if images is not None:
																					UpperCamelCase_          =  self.image_processor(_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

														if visual_prompt is not None and images is not None:
																					UpperCamelCase_          =  {
																					    "pixel_values": image_features.pixel_values,
																					    "conditional_pixel_values": prompt_features.pixel_values,
																					}
																					return encoding
														elif text is not None and images is not None:
																					UpperCamelCase_          =  image_features.pixel_values
																					return encoding
														elif text is not None:
																					return encoding
														elif visual_prompt is not None:
																					UpperCamelCase_          =  {
																					    "conditional_pixel_values": prompt_features.pixel_values,
																					}
																					return encoding
														else:
																					return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)					,   tensor_type=_SCREAMING_SNAKE_CASE		)

							def lowercase (		self: List[Any]					,   *_SCREAMING_SNAKE_CASE: Optional[Any]					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> List[str]:




														"""simple docstring"""


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

							def lowercase (		self: Optional[Any]					,   *_SCREAMING_SNAKE_CASE: List[Any]					,   **_SCREAMING_SNAKE_CASE: List[Any]		)       -> Union[str, Any]:




														"""simple docstring"""


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

							@property
							def lowercase (		self: int		)       -> str:




														"""simple docstring"""


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



							@property
							def lowercase (		self: List[str]		)       -> Union[str, Any]:




														"""simple docstring"""


														warnings.warn(
														    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,   _SCREAMING_SNAKE_CASE					,   )
														return self.image_processor








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.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_ddp.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf_dist.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.p3.16xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Union[str, Any]		)       -> List[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=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

              # 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=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


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



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

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

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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		)

