






"""simple docstring"""





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 GLPNImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=18 ,				snake_case_=30 ,				snake_case_=400 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=True ,				)		->       List[str]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    min_resolution
       UpperCamelCase__         =    max_resolution
       UpperCamelCase__         =    do_resize
       UpperCamelCase__         =    size_divisor
       UpperCamelCase__         =    do_rescale







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }







@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				List[str] 						=GLPNImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    GLPNImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(snake_case_ ,				'do_resize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'size_divisor'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'resample'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_rescale'      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       pass

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__         =    image_processing(image_inputs[0] ,				return_tensors='pt'      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCamelCase__         =    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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__         =    image_processing(image_inputs[0] ,				return_tensors='pt'      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCamelCase__         =    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 (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__         =    image_processing(image_inputs[0] ,				return_tensors='pt'      ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )












"""simple docstring"""





from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    return DownloadCommand(args.model ,	args.cache_dir ,	args.force ,	args.trust_remote_code					)





class    __lowerCamelCase    (  _a  ):

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    parser.add_parser('download'      )
       download_parser.add_argument(
           '--cache-dir' ,				type=snake_case_ ,				default=snake_case_ ,				help='Path to location to store the models'      )
       download_parser.add_argument(
           '--force' ,				action='store_true' ,				help='Force the model to be download even if already in cache-dir'      )
       download_parser.add_argument(
           '--trust-remote-code' ,				action='store_true' ,				help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,				)
       download_parser.add_argument('model' ,				type=snake_case_ ,				help='Name of the model to download'      )
       download_parser.set_defaults(func=snake_case_      )

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    model
       UpperCamelCase__         =    cache
       UpperCamelCase__         =    force
       UpperCamelCase__         =    trust_remote_code







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )
       AutoTokenizer.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )




