




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_MAPPING,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
    )
    from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    import PIL
    from PIL import Image

    from transformers import BeitImageProcessor







class __snake_case			:







   def __init__(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str=100					,						_snake_case		:						str=13					,						_snake_case		:						List[str]=30					,						_snake_case		:						Any=2					,						_snake_case		:						List[Any]=3					,						_snake_case		:						int=True					,						_snake_case		:						Dict=True					,						_snake_case		:						List[Any]=32					,						_snake_case		:						Any=4					,						_snake_case		:						int=4					,						_snake_case		:						Optional[Any]=37					,						_snake_case		:						str="gelu"					,						_snake_case		:						List[str]=0.1					,						_snake_case		:						Dict=0.1					,						_snake_case		:						Optional[int]=10					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						int=3					,						_snake_case		:						Any=None					,						_snake_case		:						List[Any]=[0, 1, 2, 3]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							100
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							image_size
        UpperCAmelCase_          =							patch_size
        UpperCAmelCase_          =							num_channels
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							type_sequence_label_size
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							scope
        UpperCAmelCase_          =							out_indices
        UpperCAmelCase_          =							num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        UpperCAmelCase_          =							(image_size // patch_size) ** 2
        UpperCAmelCase_          =							num_patches + 1







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        if self.use_labels:
             UpperCAmelCase_          =							ids_tensor([self.batch_size]					,						self.type_sequence_label_size)
             UpperCAmelCase_          =							ids_tensor([self.batch_size, self.image_size, self.image_size]					,						self.num_labels)

        UpperCAmelCase_          =							self.get_config()

        return config, pixel_values, labels, pixel_labels







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        return BeitConfig(
            vocab_size=self.vocab_size					,						image_size=self.image_size					,						patch_size=self.patch_size					,						num_channels=self.num_channels					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						is_decoder=lowerCAmelCase__					,						initializer_range=self.initializer_range					,						out_indices=self.out_indices					,						)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitModel(config=lowerCAmelCase__)
        model.to(lowerCAmelCase__)
        model.eval()
        UpperCAmelCase_          =							model(lowerCAmelCase__)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForMaskedImageModeling(config=lowerCAmelCase__)
        model.to(lowerCAmelCase__)
        model.eval()
        UpperCAmelCase_          =							model(lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length - 1, self.vocab_size))







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.type_sequence_label_size
        UpperCAmelCase_          =							BeitForImageClassification(lowerCAmelCase__)
        model.to(lowerCAmelCase__)
        model.eval()
        UpperCAmelCase_          =							model(lowerCAmelCase__					,						labels=lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        UpperCAmelCase_          =							1
        UpperCAmelCase_          =							BeitForImageClassification(lowerCAmelCase__)
        model.to(lowerCAmelCase__)
        model.eval()

        UpperCAmelCase_          =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        UpperCAmelCase_          =							model(lowerCAmelCase__					,						labels=lowerCAmelCase__)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size))







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						int					,						_snake_case		:						List[Any]					,						_snake_case		:						Dict					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							BeitForSemanticSegmentation(lowerCAmelCase__)
        model.to(lowerCAmelCase__)
        model.eval()
        UpperCAmelCase_          =							model(lowerCAmelCase__)
        self.parent.assertEqual(
            result.logits.shape					,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
        UpperCAmelCase_          =							model(lowerCAmelCase__					,						labels=lowerCAmelCase__)
        self.parent.assertEqual(
            result.logits.shape					,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.prepare_config_and_inputs()
        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							config_and_inputs
        UpperCAmelCase_          =							{'''pixel_values''': pixel_values}
        return config, inputs_dict







@require_torch
class __snake_case			(			lowerCamelCase_ ,  lowerCamelCase_ ,  unittest.TestCase		):
   UpperCAmelCase__  :    Any          =  (
       (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   UpperCAmelCase__  :    str          =  (
       {
           """feature-extraction""": BeitModel,
           """image-classification""": BeitForImageClassification,
           """image-segmentation""": BeitForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   UpperCAmelCase__  :    Optional[Any]          =  False
   UpperCAmelCase__  :    str          =  False
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							BeitModelTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=lowerCAmelCase__					,						has_text_modality=lowerCAmelCase__					,						hidden_size=37)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        self.config_tester.run_common_tests()







   @unittest.skip(reason='''BEiT does not use inputs_embeds''')
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        pass







   @require_torch_multi_gpu
   @unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase_          =							model_class(lowerCAmelCase__)
             self.assertIsInstance(model.get_input_embeddings()					,						(nn.Module))
             UpperCAmelCase_          =							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowerCAmelCase__					,						nn.Linear))







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase_          =							model_class(lowerCAmelCase__)
             UpperCAmelCase_          =							inspect.signature(model.forward)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase_          =							[*signature.parameters.keys()]

             UpperCAmelCase_          =							['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,						lowerCAmelCase__)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        if not self.model_tester.is_training:
             return

        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_          =							True

        for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if model_class in [*get_values(lowerCAmelCase__), BeitForMaskedImageModeling]:
                  continue

             UpperCAmelCase_          =							model_class(lowerCAmelCase__)
             model.to(lowerCAmelCase__)
             model.train()
             UpperCAmelCase_          =							self._prepare_for_class(lowerCAmelCase__					,						lowerCAmelCase__					,						return_labels=lowerCAmelCase__)
             UpperCAmelCase_          =							model(**lowerCAmelCase__).loss
             loss.backward()







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
             return

        UpperCAmelCase_          =							False
        UpperCAmelCase_          =							True

        for model_class in self.all_model_classes:
             # we don't test BeitForMaskedImageModeling
             if (
                 model_class in [*get_values(lowerCAmelCase__), BeitForMaskedImageModeling]
                 or not model_class.supports_gradient_checkpointing
             ):
                  continue

             UpperCAmelCase_          =							model_class(lowerCAmelCase__)
             model.gradient_checkpointing_enable()
             model.to(lowerCAmelCase__)
             model.train()
             UpperCAmelCase_          =							self._prepare_for_class(lowerCAmelCase__					,						lowerCAmelCase__					,						return_labels=lowerCAmelCase__)
             UpperCAmelCase_          =							model(**lowerCAmelCase__).loss
             loss.backward()







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_          =							_config_zero_init(lowerCAmelCase__)
        for model_class in self.all_model_classes:
             UpperCAmelCase_          =							model_class(config=lowerCAmelCase__)
             for name, param in model.named_parameters():
                  # we skip lambda parameters as these require special initial values
                  # determined by config.layer_scale_init_value
                  if "lambda" in name:
                       continue
                  if param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1e9).round() / 1e9).item()					,						[0.0, 1.0]					,						msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,						)







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_          =							BeitModel.from_pretrained(lowerCAmelCase__)
             self.assertIsNotNone(lowerCAmelCase__)







def    A						()   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image







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







   @cached_property
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''') if is_vision_available() else None







   @slow
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''').to(lowerCAmelCase__)

        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(images=lowerCAmelCase__					,						return_tensors='''pt''').pixel_values.to(lowerCAmelCase__)

        # prepare bool_masked_pos
        UpperCAmelCase_          =							torch.ones((1, 196)					,						dtype=torch.bool).to(lowerCAmelCase__)

        # forward pass
        with torch.no_grad():
             UpperCAmelCase_          =							model(pixel_values=lowerCAmelCase__					,						bool_masked_pos=lowerCAmelCase__)
        UpperCAmelCase_          =							outputs.logits

        # verify the logits
        UpperCAmelCase_          =							torch.Size((1, 196, 8192))
        self.assertEqual(logits.shape					,						lowerCAmelCase__)

        UpperCAmelCase_          =							torch.tensor(
            [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]).to(lowerCAmelCase__)

        self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]					,						lowerCAmelCase__					,						atol=1e-2))







   @slow
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''').to(lowerCAmelCase__)

        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(images=lowerCAmelCase__					,						return_tensors='''pt''').to(lowerCAmelCase__)

        # forward pass
        with torch.no_grad():
             UpperCAmelCase_          =							model(**lowerCAmelCase__)
        UpperCAmelCase_          =							outputs.logits

        # verify the logits
        UpperCAmelCase_          =							torch.Size((1, 1000))
        self.assertEqual(logits.shape					,						lowerCAmelCase__)

        UpperCAmelCase_          =							torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]).to(lowerCAmelCase__)

        self.assertTrue(torch.allclose(logits[0, :3]					,						lowerCAmelCase__					,						atol=1e-4))

        UpperCAmelCase_          =							281
        self.assertEqual(logits.argmax(-1).item()					,						lowerCAmelCase__)







   @slow
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''').to(
            lowerCAmelCase__)

        UpperCAmelCase_          =							self.default_image_processor
        UpperCAmelCase_          =							prepare_img()
        UpperCAmelCase_          =							image_processor(images=lowerCAmelCase__					,						return_tensors='''pt''').to(lowerCAmelCase__)

        # forward pass
        with torch.no_grad():
             UpperCAmelCase_          =							model(**lowerCAmelCase__)
        UpperCAmelCase_          =							outputs.logits

        # verify the logits
        UpperCAmelCase_          =							torch.Size((1, 21841))
        self.assertEqual(logits.shape					,						lowerCAmelCase__)

        UpperCAmelCase_          =							torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]).to(lowerCAmelCase__)

        self.assertTrue(torch.allclose(logits[0, :3]					,						lowerCAmelCase__					,						atol=1e-4))

        UpperCAmelCase_          =							2396
        self.assertEqual(logits.argmax(-1).item()					,						lowerCAmelCase__)







   @slow
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''')
        UpperCAmelCase_          =							model.to(lowerCAmelCase__)

        UpperCAmelCase_          =							BeitImageProcessor(do_resize=lowerCAmelCase__					,						size=640					,						do_center_crop=lowerCAmelCase__)

        UpperCAmelCase_          =							load_dataset('''hf-internal-testing/fixtures_ade20k'''					,						split='''test''')
        UpperCAmelCase_          =							Image.open(ds[0]['''file'''])
        UpperCAmelCase_          =							image_processor(images=lowerCAmelCase__					,						return_tensors='''pt''').to(lowerCAmelCase__)

        # forward pass
        with torch.no_grad():
             UpperCAmelCase_          =							model(**lowerCAmelCase__)
        UpperCAmelCase_          =							outputs.logits

        # verify the logits
        UpperCAmelCase_          =							torch.Size((1, 150, 160, 160))
        self.assertEqual(logits.shape					,						lowerCAmelCase__)

        UpperCAmelCase_          =							version.parse(PIL.__version__) < version.parse('''9.0.0''')

        if is_pillow_less_than_a:
             UpperCAmelCase_          =							torch.tensor(
                 [
                     [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                     [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                     [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                 ]					,						device=lowerCAmelCase__					,						)
        else:
             UpperCAmelCase_          =							torch.tensor(
                 [
                     [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                     [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                     [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
                 ]					,						device=lowerCAmelCase__					,						)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]					,						lowerCAmelCase__					,						atol=1e-4))







   @slow
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''')
        UpperCAmelCase_          =							model.to(lowerCAmelCase__)

        UpperCAmelCase_          =							BeitImageProcessor(do_resize=lowerCAmelCase__					,						size=640					,						do_center_crop=lowerCAmelCase__)

        UpperCAmelCase_          =							load_dataset('''hf-internal-testing/fixtures_ade20k'''					,						split='''test''')
        UpperCAmelCase_          =							Image.open(ds[0]['''file'''])
        UpperCAmelCase_          =							image_processor(images=lowerCAmelCase__					,						return_tensors='''pt''').to(lowerCAmelCase__)

        # forward pass
        with torch.no_grad():
             UpperCAmelCase_          =							model(**lowerCAmelCase__)

        UpperCAmelCase_          =							outputs.logits.detach().cpu()

        UpperCAmelCase_          =							image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__					,						target_sizes=[(500, 300)])
        UpperCAmelCase_          =							torch.Size((500, 300))
        self.assertEqual(segmentation[0].shape					,						lowerCAmelCase__)

        UpperCAmelCase_          =							image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__)
        UpperCAmelCase_          =							torch.Size((160, 160))
        self.assertEqual(segmentation[0].shape					,						lowerCAmelCase__)










"""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						,       lowerCAmelCase__						,       lowerCAmelCase__=7						,       lowerCAmelCase__=3						,       lowerCAmelCase__=18						,       lowerCAmelCase__=30						,       lowerCAmelCase__=400						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       )     ->							int:
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'height': 18, 'width': 18}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize







		def 							__A						(	self				)     ->							Optional[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	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ImageGPTImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







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







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'height': 18, 'width': 18}				)

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







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







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

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

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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

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

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







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





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					load_dataset('hf-internal-testing/fixtures_image_utils' ,			split='test'  )

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

						SCREAMING_SNAKE_CASE													=					[imagea, imagea]

						return images


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




		'''simple docstring'''







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

								SCREAMING_SNAKE_CASE													=					prepare_images()

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

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (1, 1_024)				)

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

								# test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (2, 1_024)				)

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


