"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCAmelCase_     =	logging.get_logger(__name__)



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[List[ImageInput]]:
							if isinstance(__SCREAMING_SNAKE_CASE     ,	(list, tuple)  ) and isinstance(videos[0]     ,	(list, tuple)  ) and is_valid_image(videos[0][0]  ):
														return videos

							elif isinstance(__SCREAMING_SNAKE_CASE     ,	(list, tuple)  ) and is_valid_image(videos[0]  ):
														return [videos]

							elif is_valid_image(__SCREAMING_SNAKE_CASE  ):
														return [[videos]]

							raise ValueError(F"""Could not make batched video from {videos}"""  )




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["pixel_values"]





				def __init__(      self							:							Optional[Any]      ,    _A							:							bool = True      ,    _A							:							Dict[str, int] = None      ,    _A							:							PILImageResampling = PILImageResampling.BILINEAR      ,    _A							:							bool = True      ,    _A							:							Dict[str, int] = None      ,    _A							:							bool = True      ,    _A							:							Union[int, float] = 1 / 2_5_5      ,    _A							:							bool = True      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    size if size is not None else {"""shortest_edge""": 2_2_4}
											_SCREAMING_SNAKE_CASE		:						int			  =    get_size_dict(_A      ,    default_to_square=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_size_dict(_A      ,    param_name="""crop_size""")

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    do_resize
											_SCREAMING_SNAKE_CASE		:						Any			  =    size
											_SCREAMING_SNAKE_CASE		:						str			  =    do_center_crop
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    crop_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    resample
											_SCREAMING_SNAKE_CASE		:						str			  =    do_rescale
											_SCREAMING_SNAKE_CASE		:						Dict			  =    rescale_factor
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    do_normalize
											_SCREAMING_SNAKE_CASE		:						int			  =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    image_std if image_std is not None else IMAGENET_STANDARD_STD





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							np.ndarray      ,    _A							:							Dict[str, int]      ,    _A							:							PILImageResampling = PILImageResampling.BILINEAR      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							int      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    get_size_dict(_A      ,    default_to_square=_A)
											if "shortest_edge" in size:
																		_SCREAMING_SNAKE_CASE		:						str			  =    get_resize_output_image_size(_A      ,    size["""shortest_edge"""]      ,    default_to_square=_A)
											elif "height" in size and "width" in size:
																		_SCREAMING_SNAKE_CASE		:						str			  =    (size["""height"""], size["""width"""])
											else:
																		raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
											return resize(_A      ,    size=_A      ,    resample=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							np.ndarray      ,    _A							:							Dict[str, int]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_size_dict(_A)
											if "height" not in size or "width" not in size:
																		raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
											return center_crop(_A      ,    size=(size["""height"""], size["""width"""])      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							np.ndarray      ,    _A							:							Union[int, float]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							Union[str, Any]      ,    ):



											"""simple docstring"""







											return rescale(_A      ,    scale=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							np.ndarray      ,    _A							:							Union[float, List[float]]      ,    _A							:							Union[float, List[float]]      ,    _A							:							Optional[Union[str, ChannelDimension]] = None      ,    **_A							:							List[str]      ,    ):



											"""simple docstring"""







											return normalize(_A      ,    mean=_A      ,    std=_A      ,    data_format=_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							ImageInput      ,    _A							:							bool = None      ,    _A							:							Dict[str, int] = None      ,    _A							:							PILImageResampling = None      ,    _A							:							bool = None      ,    _A							:							Dict[str, int] = None      ,    _A							:							bool = None      ,    _A							:							float = None      ,    _A							:							bool = None      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    _A							:							Optional[ChannelDimension] = ChannelDimension.FIRST      ,    ):



											"""simple docstring"""







											if do_resize and size is None or resample is None:
																		raise ValueError("""Size and resample must be specified if do_resize is True.""")

											if do_center_crop and crop_size is None:
																		raise ValueError("""Crop size must be specified if do_center_crop is True.""")

											if do_rescale and rescale_factor is None:
																		raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    to_numpy_array(_A)

											if do_resize:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    self.resize(image=_A      ,    size=_A      ,    resample=_A)

											if do_center_crop:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.center_crop(_A      ,    size=_A)

											if do_rescale:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.rescale(image=_A      ,    scale=_A)

											if do_normalize:
																		_SCREAMING_SNAKE_CASE		:						int			  =    self.normalize(image=_A      ,    mean=_A      ,    std=_A)

											_SCREAMING_SNAKE_CASE		:						str			  =    to_channel_dimension_format(_A      ,    _A)
											return image





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							ImageInput      ,    _A							:							bool = None      ,    _A							:							Dict[str, int] = None      ,    _A							:							PILImageResampling = None      ,    _A							:							bool = None      ,    _A							:							Dict[str, int] = None      ,    _A							:							bool = None      ,    _A							:							float = None      ,    _A							:							bool = None      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    _A							:							Optional[Union[float, List[float]]] = None      ,    _A							:							Optional[Union[str, TensorType]] = None      ,    _A							:							ChannelDimension = ChannelDimension.FIRST      ,    **_A							:							List[Any]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE		:						Any			  =    do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE		:						str			  =    image_std if image_std is not None else self.image_std

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    size if size is not None else self.size
											_SCREAMING_SNAKE_CASE		:						int			  =    get_size_dict(_A      ,    default_to_square=_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    get_size_dict(_A      ,    param_name="""crop_size""")

											if not valid_images(_A):
																		raise ValueError(
																		    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																		    """torch.Tensor, tf.Tensor or jax.ndarray.""")

											_SCREAMING_SNAKE_CASE		:						Dict			  =    make_batched(_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    [
											    [
											        self._preprocess_image(
											            image=_A      ,    do_resize=_A      ,    size=_A      ,    resample=_A      ,    do_center_crop=_A      ,    crop_size=_A      ,    do_rescale=_A      ,    rescale_factor=_A      ,    do_normalize=_A      ,    image_mean=_A      ,    image_std=_A      ,    data_format=_A      ,    )
											        for img in video
											    ]
											    for video in videos
											]

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    {"""pixel_values""": videos}
											return BatchFeature(data=_A      ,    tensor_type=_A)



"""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)


