
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ViTMAEForPreTraining, ViTMAEModel
   from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor







class 					lowerCamelCase		:





     def __init__(self				:   Union[str, Any]    ,   _A				:   Tuple    ,   _A				:   Optional[int]=1_3    ,   _A				:   Any=3_0    ,   _A				:   Any=2    ,   _A				:   Dict=3    ,   _A				:   Union[str, Any]=True    ,   _A				:   int=True    ,   _A				:   str=3_2    ,   _A				:   Any=5    ,   _A				:   Optional[Any]=4    ,   _A				:   List[str]=3_7    ,   _A				:   Dict="gelu"    ,   _A				:   str=0.1    ,   _A				:   Dict=0.1    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   List[str]=3    ,   _A				:   Dict=0.6    ,   _A				:   Dict=None    ,   )						->						Optional[int]:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= image_size
         snake_case								= patch_size
         snake_case								= num_channels
         snake_case								= is_training
         snake_case								= use_labels
         snake_case								= hidden_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= type_sequence_label_size
         snake_case								= initializer_range
         snake_case								= mask_ratio
         snake_case								= scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         snake_case								= (image_size // patch_size) ** 2
         snake_case								= int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )





     def 		UpperCAmelCase(self				:   Dict       )						->						Optional[int]:
         snake_case								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case								= None
         if self.use_labels:
             snake_case								= ids_tensor([self.batch_size]    ,   self.type_sequence_label_size       )

         snake_case								= self.get_config()

         return config, pixel_values, labels





     def 		UpperCAmelCase(self				:   Any       )						->						Optional[Any]:
         return ViTMAEConfig(
             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=_A    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   int    ,   _A				:   Union[str, Any]    ,   _A				:   int       )						->						int:
         snake_case								= ViTMAEModel(config=_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]    ,   _A				:   Tuple    ,   _A				:   List[str]       )						->						Optional[Any]:
         snake_case								= ViTMAEForPreTraining(_A       )
         model.to(_A       )
         model.eval()
         snake_case								= model(_A       )
         snake_case								= (self.image_size // self.patch_size) ** 2
         snake_case								= self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)       )

         # test greyscale images
         snake_case								= 1
         snake_case								= ViTMAEForPreTraining(_A       )
         model.to(_A       )
         model.eval()
         snake_case								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case								= model(_A       )
         snake_case								= self.patch_size**2
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)       )





     def 		UpperCAmelCase(self				:   Any       )						->						Any:
         snake_case								= self.prepare_config_and_inputs()
         snake_case					,							snake_case					,							snake_case								= config_and_inputs
         snake_case								= {"pixel_values": pixel_values}
         return config, inputs_dict







@require_torch
class 					lowerCamelCase		(  A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Dict									=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     UpperCAmelCase__    :							Optional[Any]									=		{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

     UpperCAmelCase__    :							str									=		False
     UpperCAmelCase__    :							Dict									=		False
     UpperCAmelCase__    :							int									=		False
     UpperCAmelCase__    :							Union[str, Any]									=		False





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						int:
         snake_case								= ViTMAEModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   has_text_modality=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   str       )						->						Any:
         self.config_tester.run_common_tests()





     @unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[str]:
         pass





     def 		UpperCAmelCase(self				:   Any       )						->						Any:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

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





     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case								= ["pixel_values"]
             self.assertListEqual(arg_names[:1]    ,   _A       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Any       )						->						str:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_A       )





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[int]    ,   _A				:   List[str]    ,   _A				:   Optional[int]       )						->						Union[str, Any]:
         # make masks reproducible
         np.random.seed(2       )

         snake_case								= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         snake_case								= torch.from_numpy(_A       )

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         snake_case								= pt_noise

         super().check_pt_tf_models(_A    ,   _A    ,   _A       )





     def 		UpperCAmelCase(self				:   Any       )						->						List[Any]:
         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             snake_case								= model_class(_A       )
             model.to(_A       )
             model.eval()
             # make random mask reproducible
             torch.manual_seed(2       )
             with torch.no_grad():
                 snake_case								= model(**self._prepare_for_class(_A    ,   _A       )       )

             snake_case								= outputs[0].cpu().numpy()
             snake_case								= 0

             with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(_A       )
                 snake_case								= model_class.from_pretrained(_A       )
                 model.to(_A       )
                 # make random mask reproducible
                 torch.manual_seed(2       )
                 with torch.no_grad():
                     snake_case								= model(**self._prepare_for_class(_A    ,   _A       )       )

                 # Make sure we don't have nans
                 snake_case								= after_outputs[0].cpu().numpy()
                 snake_case								= 0
                 snake_case								= np.amax(np.abs(out_a - out_a       )       )
                 self.assertLessEqual(_A    ,   1E-5       )





     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         pass





     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
     def 		UpperCAmelCase(self				:   List[str]       )						->						Union[str, Any]:
         pass





     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
     def 		UpperCAmelCase(self				:   int       )						->						Tuple:
         pass





     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"       )
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         pass





     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         pass





     @slow
     def 		UpperCAmelCase(self				:   str       )						->						Dict:
         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= ViTMAEModel.from_pretrained(_A       )
             self.assertIsNotNone(_A       )



def 						lowercase_      (       )							->							Tuple:
    """simple docstring"""

    snake_case								= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    return image







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





     @cached_property
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						int:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2       )

         snake_case								= ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       ).to(_A       )

         snake_case								= self.default_image_processor
         snake_case								= prepare_img()
         snake_case								= image_processor(images=_A    ,   return_tensors="pt"       ).to(_A       )

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         snake_case								= ViTMAEConfig()
         snake_case								= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(1, num_patches)       )

         # forward pass
         with torch.no_grad():
             snake_case								= model(**_A    ,   noise=torch.from_numpy(_A       ).to(device=_A       )       )

         # verify the logits
         snake_case								= torch.Size((1, 1_9_6, 7_6_8)       )
         self.assertEqual(outputs.logits.shape    ,   _A       )

         snake_case								= torch.tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]       )

         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,   expected_slice.to(_A       )    ,   atol=1E-4       )       )



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

