

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=7						,       a=3						,       a=18						,       a=30						,       a=400						,       a=True						,       a=None						,       a=True						,       a=None						,       a=True						,       ):
         lowercase__   :       List[str]				=	size if size is not None else {"""shortest_edge""": 20}
         lowercase__   :       Union[str, Any]				=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         lowercase__   :       Tuple				=	parent
         lowercase__   :       Tuple				=	batch_size
         lowercase__   :       List[str]				=	num_channels
         lowercase__   :       Any				=	image_size
         lowercase__   :       str				=	min_resolution
         lowercase__   :       Dict				=	max_resolution
         lowercase__   :       Optional[int]				=	do_resize
         lowercase__   :       int				=	size
         lowercase__   :       List[Any]				=	do_center_crop
         lowercase__   :       int				=	crop_size
         lowercase__   :       Dict				=	do_flip_channel_order





    def 				snake_case_    (				self):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }



@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE__ (_a		,	unittest.TestCase					):
    __lowerCamelCase						:	Any														=					MobileViTImageProcessor if is_vision_available() else None





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	MobileViTImageProcessingTester(self)





    @property
    def 				snake_case_    (				self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(snake_case_						,       'do_resize'))
         self.assertTrue(hasattr(snake_case_						,       'size'))
         self.assertTrue(hasattr(snake_case_						,       'do_center_crop'))
         self.assertTrue(hasattr(snake_case_						,       'center_crop'))
         self.assertTrue(hasattr(snake_case_						,       'do_flip_channel_order'))





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size						,       {'shortest_edge': 20})
         self.assertEqual(image_processor.crop_size						,       {'height': 18, 'width': 18})

         lowercase__   :       Dict				=	self.image_processing_class.from_dict(self.image_processor_dict						,       size=42						,       crop_size=84)
         self.assertEqual(image_processor.size						,       {'shortest_edge': 42})
         self.assertEqual(image_processor.crop_size						,       {'height': 84, 'width': 84})





    def 				snake_case_    (				self):
         pass





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       Tuple				=	self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__   :       int				=	prepare_image_inputs(self.image_processor_tester						,       equal_resolution=snake_case_)
         for image in image_inputs:
              self.assertIsInstance(snake_case_						,       Image.Image)

         # Test not batched input
         lowercase__   :       Dict				=	image_processing(image_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       List[str]				=	image_processing(snake_case_						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       int				=	self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__   :       Optional[int]				=	prepare_image_inputs(self.image_processor_tester						,       equal_resolution=snake_case_						,       numpify=snake_case_)
         for image in image_inputs:
              self.assertIsInstance(snake_case_						,       np.ndarray)

         # Test not batched input
         lowercase__   :       Optional[Any]				=	image_processing(image_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       str				=	image_processing(snake_case_						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )





    def 				snake_case_    (				self):
         # Initialize image_processing
         lowercase__   :       List[str]				=	self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__   :       str				=	prepare_image_inputs(self.image_processor_tester						,       equal_resolution=snake_case_						,       torchify=snake_case_)
         for image in image_inputs:
              self.assertIsInstance(snake_case_						,       torch.Tensor)

         # Test not batched input
         lowercase__   :       List[Any]				=	image_processing(image_inputs[0]						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )

         # Test batched
         lowercase__   :       Optional[Any]				=	image_processing(snake_case_						,       return_tensors='pt').pixel_values
         self.assertEqual(
             encoded_images.shape						,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )						,       )




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

