


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     lowercase__  (					unittest.TestCase		):






		def __init__(					self      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					str=7		,						_lowercase      :					int=3		,						_lowercase      :					Union[str, Any]=18		,						_lowercase      :					Optional[int]=30		,						_lowercase      :					Union[str, Any]=4_00		,						_lowercase      :					Optional[Any]=True		,						_lowercase      :					List[str]=None		,						_lowercase      :					Optional[int]=True		,						):




									"""simple docstring"""







									UpperCAmelCase__										=     size if size is not None else {"height": 18, "width": 18}
									UpperCAmelCase__										=     parent
									UpperCAmelCase__										=     batch_size
									UpperCAmelCase__										=     num_channels
									UpperCAmelCase__										=     image_size
									UpperCAmelCase__										=     min_resolution
									UpperCAmelCase__										=     max_resolution
									UpperCAmelCase__										=     do_resize
									UpperCAmelCase__										=     size
									UpperCAmelCase__										=     do_normalize






		def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




									"""simple docstring"""







									return {
									    # here we create 2 clusters for the sake of simplicity
									    "clusters": np.asarray(
									        [
									            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
									            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
									        ] ),
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									}




@require_torch
@require_vision
class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
		A__=  ImageGPTImageProcessor if is_vision_available() else None






		def 			_UpperCAmelCase  (					self      :					int ):




									"""simple docstring"""







									UpperCAmelCase__										=     ImageGPTImageProcessingTester(self )






		@property
		def 			_UpperCAmelCase  (					self      :					Dict ):




									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()






		def 			_UpperCAmelCase  (					self      :					List[Any] ):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(_lowercase		,						"clusters" ) )
									self.assertTrue(hasattr(_lowercase		,						"do_resize" ) )
									self.assertTrue(hasattr(_lowercase		,						"size" ) )
									self.assertTrue(hasattr(_lowercase		,						"do_normalize" ) )






		def 			_UpperCAmelCase  (					self      :					List[Any] ):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processing_class.from_dict(self.image_processor_dict )
									self.assertEqual(image_processor.size		,						{"height": 18, "width": 18} )

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






		def 			_UpperCAmelCase  (					self      :					Optional[int] ):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )
									UpperCAmelCase__										=     json.loads(image_processor.to_json_string() )
									for key, value in self.image_processor_dict.items():
																if key == "clusters":
																							self.assertTrue(np.array_equal(_lowercase		,						obj[key] ) )
																else:
																							self.assertEqual(obj[key]		,						_lowercase )






		def 			_UpperCAmelCase  (					self      :					List[str] ):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )

									with tempfile.TemporaryDirectory() as tmpdirname:
																UpperCAmelCase__										=     os.path.join(_lowercase		,						"image_processor.json" )
																image_processor_first.to_json_file(_lowercase )
																UpperCAmelCase__										=     self.image_processing_class.from_json_file(_lowercase ).to_dict()

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






		def 			_UpperCAmelCase  (					self      :					List[Any] ):




									"""simple docstring"""







									UpperCAmelCase__										=     self.image_processing_class(**self.image_processor_dict )

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

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






		@unittest.skip("ImageGPT requires clusters at initialization" )
		def 			_UpperCAmelCase  (					self      :					int ):




									"""simple docstring"""







									pass





def __UpperCAmelCase       (		)						->					int:



							'''simple docstring'''





							UpperCAmelCase__										=     load_dataset("hf-internal-testing/fixtures_image_utils" ,						split="test"			)

							UpperCAmelCase__										=     Image.open(dataset[4]["file"]			)
							UpperCAmelCase__										=     Image.open(dataset[5]["file"]			)

							UpperCAmelCase__										=     [imagea, imagea]

							return images




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






		@slow
		def 			_UpperCAmelCase  (					self      :					Tuple ):




									"""simple docstring"""







									UpperCAmelCase__										=     ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )

									UpperCAmelCase__										=     prepare_images()

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

									self.assertIsInstance(encoding.input_ids		,						torch.LongTensor )
									self.assertEqual(encoding.input_ids.shape		,						(1, 10_24) )

									UpperCAmelCase__										=     [3_06, 1_91, 1_91]
									self.assertEqual(encoding.input_ids[0, :3].tolist()		,						_lowercase )

									# test batched
									UpperCAmelCase__										=     image_processing(_lowercase		,						return_tensors="pt" )

									self.assertIsInstance(encoding.input_ids		,						torch.LongTensor )
									self.assertEqual(encoding.input_ids.shape		,						(2, 10_24) )

									UpperCAmelCase__										=     [3_03, 13, 13]
									self.assertEqual(encoding.input_ids[1, -3:].tolist()		,						_lowercase )









import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Dict		,						_lowercase      :					int		,						_lowercase      :					List[Any]=None		,						_lowercase      :					str=True		,						_lowercase      :					str=None		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     config_class
         UpperCAmelCase__										=     has_text_modality
         UpperCAmelCase__										=     kwargs
         UpperCAmelCase__										=     common_properties






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         UpperCAmelCase__										=     (
             ["hidden_size", "num_attention_heads", "num_hidden_layers"]
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
                common_properties.extend(["vocab_size"] )

         # Test that config has the common properties as getters
         for prop in common_properties:
                self.parent.assertTrue(hasattr(_lowercase		,						_lowercase )		,						msg=F"""`{prop}` does not exist""" )

         # Test that config has the common properties as setter
         for idx, name in enumerate(_lowercase ):
                try:
                       setattr(_lowercase		,						_lowercase		,						_lowercase )
                       self.parent.assertEqual(
                           getattr(_lowercase		,						_lowercase )		,						_lowercase		,						msg=F"""`{name} value {idx} expected, but was {getattr(_lowercase		,						_lowercase )}""" )
                except NotImplementedError:
                       # Some models might not be able to implement setters for common_properties
                       # In that case, a NotImplementedError is raised
                       pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(_lowercase ):
                try:
                       UpperCAmelCase__										=     self.config_class(**{name: idx} )
                       self.parent.assertEqual(
                           getattr(_lowercase		,						_lowercase )		,						_lowercase		,						msg=F"""`{name} value {idx} expected, but was {getattr(_lowercase		,						_lowercase )}""" )
                except NotImplementedError:
                       # Some models might not be able to implement setters for common_properties
                       # In that case, a NotImplementedError is raised
                       pass






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )
         UpperCAmelCase__										=     json.loads(config.to_json_string() )
         for key, value in self.inputs_dict.items():
                self.parent.assertEqual(obj[key]		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCAmelCase__										=     os.path.join(_lowercase		,						"config.json" )
                config_first.to_json_file(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_json_file(_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
                config_first.save_pretrained(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_pretrained(_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict )

         UpperCAmelCase__										=     "test"
         with tempfile.TemporaryDirectory() as tmpdirname:
                UpperCAmelCase__										=     os.path.join(_lowercase		,						_lowercase )
                config_first.save_pretrained(_lowercase )
                UpperCAmelCase__										=     self.config_class.from_pretrained(_lowercase		,						subfolder=_lowercase )

         self.parent.assertEqual(config_second.to_dict()		,						config_first.to_dict() )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.config_class(**self.inputs_dict		,						num_labels=5 )
         self.parent.assertEqual(len(config.idalabel )		,						5 )
         self.parent.assertEqual(len(config.labelaid )		,						5 )

         UpperCAmelCase__										=     3
         self.parent.assertEqual(len(config.idalabel )		,						3 )
         self.parent.assertEqual(len(config.labelaid )		,						3 )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         if self.config_class.is_composition:
                return
         UpperCAmelCase__										=     self.config_class()
         self.parent.assertIsNotNone(_lowercase )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     copy.deepcopy(_lowercase )
         UpperCAmelCase__										=     self.config_class(**_lowercase )
         UpperCAmelCase__										=     []
         for key, value in config_common_kwargs.items():
                if key == "torch_dtype":
                       if not is_torch_available():
                              continue
                       else:
                              import torch

                              if config.torch_dtype != torch.floataa:
                                     wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) )
                elif getattr(_lowercase		,						_lowercase ) != value:
                       wrong_values.append((key, getattr(_lowercase		,						_lowercase ), value) )

         if len(_lowercase ) > 0:
                UpperCAmelCase__										=     "\n".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
                raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()





