


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   a__	(						unittest.TestCase    ):

       def __init__(   self      :     int      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Union[str, Any]=7      ,							lowerCamelCase_      :     Optional[Any]=3      ,							lowerCamelCase_      :     str=1_8      ,							lowerCamelCase_      :     Union[str, Any]=3_0      ,							lowerCamelCase_      :     str=4_0_0      ,							lowerCamelCase_      :     Optional[Any]=True      ,							lowerCamelCase_      :     int=None      ,							lowerCamelCase_      :     Tuple=True      ,							):
            a_    :     Optional[Any]										=    size if size is not None else {"""height""": 1_8, """width""": 1_8}
            a_    :     Dict										=    parent
            a_    :     List[str]										=    batch_size
            a_    :     Optional[int]										=    num_channels
            a_    :     Dict										=    image_size
            a_    :     Union[str, Any]										=    min_resolution
            a_    :     str										=    max_resolution
            a_    :     Any										=    do_resize
            a_    :     str										=    size
            a_    :     Any										=    do_normalize

       def 						UpperCAmelCase(   self      :     Tuple    ):
            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   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Optional[Any]							    =     ImageGPTImageProcessor if is_vision_available() else None

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     int										=    ImageGPTImageProcessingTester(self    )

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     str										=    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 						UpperCAmelCase(   self      :     int    ):
            a_    :     Union[str, Any]										=    self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size      ,							{"""height""": 1_8, """width""": 1_8}    )

            a_    :     Union[str, Any]										=    self.image_processing_class.from_dict(self.image_processor_dict      ,							size=4_2    )
            self.assertEqual(image_processor.size      ,							{"""height""": 4_2, """width""": 4_2}    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     Dict										=    self.image_processing_class(**self.image_processor_dict    )
            a_    :     Any										=    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 						UpperCAmelCase(   self      :     Any    ):
            a_    :     str										=    self.image_processing_class(**self.image_processor_dict    )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 a_    :     Dict										=    os.path.join(lowerCamelCase_      ,							"""image_processor.json"""    )
                 image_processor_first.to_json_file(lowerCamelCase_    )
                 a_    :     int										=    self.image_processing_class.from_json_file(lowerCamelCase_    ).to_dict()

            a_    :     List[Any]										=    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 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     int										=    self.image_processing_class(**self.image_processor_dict    )

            with tempfile.TemporaryDirectory() as tmpdirname:
                 image_processor_first.save_pretrained(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.image_processing_class.from_pretrained(lowerCamelCase_    ).to_dict()

            a_    :     Tuple										=    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 						UpperCAmelCase(   self      :     Optional[int]    ):
            pass



def  _a			(						):
     a_    :     Tuple										=    load_dataset("""hf-internal-testing/fixtures_image_utils"""  ,  split="""test""" )

     a_    :     List[Any]										=    Image.open(dataset[4]["""file"""] )
     a_    :     Optional[Any]										=    Image.open(dataset[5]["""file"""] )

     a_    :     List[str]										=    [imagea, imagea]

     return images


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

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Dict										=    ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""    )

            a_    :     List[Any]										=    prepare_images()

            # test non-batched
            a_    :     Union[str, Any]										=    image_processing(images[0]      ,							return_tensors="""pt"""    )

            self.assertIsInstance(encoding.input_ids      ,							torch.LongTensor    )
            self.assertEqual(encoding.input_ids.shape      ,							(1, 1_0_2_4)    )

            a_    :     Dict										=    [3_0_6, 1_9_1, 1_9_1]
            self.assertEqual(encoding.input_ids[0, :3].tolist()      ,							lowerCamelCase_    )

            # test batched
            a_    :     Dict										=    image_processing(lowerCamelCase_      ,							return_tensors="""pt"""    )

            self.assertIsInstance(encoding.input_ids      ,							torch.LongTensor    )
            self.assertEqual(encoding.input_ids.shape      ,							(2, 1_0_2_4)    )

            a_    :     Optional[int]										=    [3_0_3, 1_3, 1_3]
            self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,							lowerCamelCase_    )






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase        =     logging.get_logger(__name__)

__lowerCamelCase        =     {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):
       lowerCamelCase__:     str							    =     """convnextv2"""

       def __init__(   self      :     Tuple      ,							lowerCamelCase_      :     str=3      ,							lowerCamelCase_      :     Tuple=4      ,							lowerCamelCase_      :     Any=4      ,							lowerCamelCase_      :     Optional[int]=None      ,							lowerCamelCase_      :     Optional[int]=None      ,							lowerCamelCase_      :     Optional[int]="gelu"      ,							lowerCamelCase_      :     Optional[Any]=0.0_2      ,							lowerCamelCase_      :     str=1E-12      ,							lowerCamelCase_      :     Dict=0.0      ,							lowerCamelCase_      :     Union[str, Any]=2_2_4      ,							lowerCamelCase_      :     Dict=None      ,							lowerCamelCase_      :     Optional[Any]=None      ,							**lowerCamelCase_      :     List[Any]      ,							):
            super().__init__(**lowerCamelCase_    )

            a_    :     Dict										=    num_channels
            a_    :     Optional[int]										=    patch_size
            a_    :     int										=    num_stages
            a_    :     Any										=    [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
            a_    :     Any										=    [3, 3, 9, 3] if depths is None else depths
            a_    :     Any										=    hidden_act
            a_    :     Dict										=    initializer_range
            a_    :     Optional[Any]										=    layer_norm_eps
            a_    :     int										=    drop_path_rate
            a_    :     Optional[Any]										=    image_size
            a_    :     Dict										=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,							len(self.depths    ) + 1    )]
            a_			,			a_    :     int										=    get_aligned_output_features_output_indices(
                out_features=lowerCamelCase_      ,							out_indices=lowerCamelCase_      ,							stage_names=self.stage_names    )



