

from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def 			UpperCamelCase_(				_snake_case  :    Any      ):




			"""simple docstring"""






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






class       __magic_name__       (     _UpperCamelCase ):





			@staticmethod
			def 						__magic_name__       (						__snake_case   )  ->					Tuple:




						'''simple docstring'''
						__a     =parser.add_parser('download'   )
						download_parser.add_argument(
						    '--cache-dir'   ,    type=__a   ,    default=__a   ,    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=__a   ,    help='Name of the model to download'   )
						download_parser.set_defaults(func=__a   )





			def __init__(						self   ,    __snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case   )  ->					Tuple:




						'''simple docstring'''
						__a     =model
						__a     =cache
						__a     =force
						__a     =trust_remote_code





			def 						__magic_name__       (						self   )  ->					Tuple:




						'''simple docstring'''
						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   )













"""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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							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						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							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 :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							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						)
