
"""simple docstring"""


from argparse import ArgumentParser

from . import BaseTransformersCLICommand






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	return DownloadCommand(args.model	,      args.cache_dir	,      args.force	,      args.trust_remote_code     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							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     :      Dict  ,A_     :      str  ,A_     :      str  ,A_     :      bool  ,A_     :      bool      )     ->   Union[str, Any]:
							A						   =   model
							A						   =   cache
							A						   =   force
							A						   =   trust_remote_code




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							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 json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )