
"""simple docstring"""




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=30	,    _a=2	,    _a=3	,    _a=True	,    _a=True	,    _a=32	,    _a=2	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=10	,    _a=0.0_2	,    _a=3	,    _a=0.6	,    _a=None	,    ):
          lowercase    :				List[str]       		=							parent
          lowercase    :				List[Any]       		=							batch_size
          lowercase    :				str       		=							image_size
          lowercase    :				List[Any]       		=							patch_size
          lowercase    :				List[str]       		=							num_channels
          lowercase    :				Union[str, Any]       		=							is_training
          lowercase    :				List[str]       		=							use_labels
          lowercase    :				Tuple       		=							hidden_size
          lowercase    :				str       		=							num_hidden_layers
          lowercase    :				List[Any]       		=							num_attention_heads
          lowercase    :				Optional[Any]       		=							intermediate_size
          lowercase    :				List[str]       		=							hidden_act
          lowercase    :				str       		=							hidden_dropout_prob
          lowercase    :				List[str]       		=							attention_probs_dropout_prob
          lowercase    :				Union[str, Any]       		=							type_sequence_label_size
          lowercase    :				List[str]       		=							initializer_range
          lowercase    :				Optional[int]       		=							mask_ratio
          lowercase    :				Optional[int]       		=							scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          lowercase    :				Optional[Any]       		=							(image_size // patch_size) ** 2
          lowercase    :				Any       		=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowercase    :				Tuple       		=							None
          if self.use_labels:
                 lowercase    :				str       		=							ids_tensor([self.batch_size]	,    self.type_sequence_label_size				)

          lowercase    :				List[Any]       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          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	,    decoder_hidden_size=self.hidden_size	,    decoder_num_hidden_layers=self.num_hidden_layers	,    decoder_num_attention_heads=self.num_attention_heads	,    decoder_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=__lowercase	,    initializer_range=self.initializer_range	,    mask_ratio=self.mask_ratio	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Any       		=							TFViTMAEModel(config=__lowercase				)
          lowercase    :				Union[str, Any]       		=							model(__lowercase	,    training=__lowercase				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							TFViTMAEForPreTraining(__lowercase				)
          lowercase    :				str       		=							model(__lowercase	,    training=__lowercase				)
          # expected sequence length = num_patches
          lowercase    :				List[str]       		=							(self.image_size // self.patch_size) ** 2
          lowercase    :				Union[str, Any]       		=							self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, num_patches, expected_num_channels)				)

          # test greyscale images
          lowercase    :				List[str]       		=							1
          lowercase    :				List[str]       		=							TFViTMAEForPreTraining(__lowercase				)

          lowercase    :				int       		=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
          lowercase    :				Dict       		=							model(__lowercase	,    training=__lowercase				)
          lowercase    :				List[str]       		=							self.patch_size**2
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, num_patches, expected_num_channels)				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.prepare_config_and_inputs()
          (lowercase)    :				List[str]       		=							config_and_inputs
          lowercase    :				Dict       		=							{'''pixel_values''': pixel_values}
          return config, inputs_dict


@require_tf
class 		a__						(      UpperCAmelCase_,			UpperCAmelCase_,			unittest.TestCase						):
   __lowerCAmelCase            =				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   __lowerCAmelCase            =				{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							TFViTMAEModelTester(self				)
          lowercase    :				List[str]       		=							ConfigTester(self	,    config_class=__lowercase	,    has_text_modality=__lowercase	,    hidden_size=37				)
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   @unittest.skip(reason="ViTMAE does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				List[Any]       		=							model_class(__lowercase				)
                 self.assertIsInstance(model.get_input_embeddings()	,    (tf.keras.layers.Layer)				)
                 lowercase    :				Optional[Any]       		=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__lowercase	,    tf.keras.layers.Layer				)				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Tuple       		=							model_class(__lowercase				)
                 lowercase    :				int       		=							inspect.signature(model.call				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase    :				Optional[int]       		=							[*signature.parameters.keys()]

                 lowercase    :				Any       		=							['''pixel_values''']
                 self.assertListEqual(arg_names[:1]	,    __lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__lowercase				)
   def 			__magic_name__			(			self				):
          # make the mask reproducible
          np.random.seed(2				)

          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Tuple       		=							int((config.image_size // config.patch_size) ** 2				)
          lowercase    :				Optional[int]       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

          for model_class in self.all_model_classes:
                 lowercase    :				str       		=							model_class(__lowercase				)
                 lowercase    :				Optional[int]       		=							self._prepare_for_class(__lowercase	,    __lowercase				)

                 lowercase    :				Dict       		=							model(__lowercase	,    noise=__lowercase				)

                 lowercase    :				int       		=							copy.deepcopy(self._prepare_for_class(__lowercase	,    __lowercase				)				)
                 lowercase    :				Union[str, Any]       		=							model(**__lowercase	,    noise=__lowercase				)
                 lowercase    :				Tuple       		=							outputs_dict[0].numpy()
                 lowercase    :				Union[str, Any]       		=							outputs_keywords[0].numpy()

                 self.assertLess(np.sum(np.abs(output_dict - output_keywords				)				)	,    1E-6				)
   def 			__magic_name__			(			self				):
          # make the mask reproducible
          np.random.seed(2				)

          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				int       		=							int((config.image_size // config.patch_size) ** 2				)
          lowercase    :				str       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

          def prepare_numpy_arrays(_a				):
                 lowercase    :				Optional[int]       		=							{}
                 for k, v in inputs_dict.items():
                        if tf.is_tensor(__lowercase				):
                               lowercase    :				Optional[Any]       		=							v.numpy()
                        else:
                               lowercase    :				Optional[int]       		=							np.array(__lowercase				)

                 return inputs_np_dict

          for model_class in self.all_model_classes:
                 lowercase    :				int       		=							model_class(__lowercase				)

                 lowercase    :				Tuple       		=							self._prepare_for_class(__lowercase	,    __lowercase				)
                 lowercase    :				Any       		=							prepare_numpy_arrays(__lowercase				)

                 lowercase    :				Any       		=							model(__lowercase	,    noise=__lowercase				)
                 lowercase    :				Tuple       		=							model(**__lowercase	,    noise=__lowercase				)
                 self.assert_outputs_same(__lowercase	,    __lowercase				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          # make masks reproducible
          np.random.seed(2				)

          lowercase    :				Any       		=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2				)
          lowercase    :				Optional[Any]       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
          lowercase    :				Dict       		=							tf.constant(__lowercase				)

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          lowercase    :				Any       		=							tf_noise

          super().check_pt_tf_models(__lowercase	,    __lowercase	,    __lowercase				)
   def 			__magic_name__			(			self				):
          # make mask reproducible
          np.random.seed(2				)

          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Optional[int]       		=							{
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__				),)
              for module_member_name in dir(__lowercase				)
              if module_member_name.endswith("MainLayer"				)
              # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
              and module_member_name[: -len("MainLayer"				)] == model_class.__name__[: -len("Model"				)]
              for module_member in (getattr(__lowercase	,    __lowercase				),)
              if isinstance(__lowercase	,    __lowercase				)
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(__lowercase	,    "_keras_serializable"	,    __lowercase				)
          }

          lowercase    :				Union[str, Any]       		=							int((config.image_size // config.patch_size) ** 2				)
          lowercase    :				List[str]       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
          lowercase    :				str       		=							tf.convert_to_tensor(__lowercase				)
          inputs_dict.update({"noise": noise}				)

          for main_layer_class in tf_main_layer_classes:
                 lowercase    :				Optional[int]       		=							main_layer_class(__lowercase				)

                 lowercase    :				Optional[Any]       		=							{
                     name: tf.keras.Input(tensor.shape[1:]	,    dtype=tensor.dtype				) for name, tensor in inputs_dict.items()
                 }

                 lowercase    :				Dict       		=							tf.keras.Model(__lowercase	,    outputs=main_layer(__lowercase				)				)
                 lowercase    :				str       		=							model(__lowercase				)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                        lowercase    :				str       		=							os.path.join(__lowercase	,    "keras_model.h5"				)
                        model.save(__lowercase				)
                        lowercase    :				List[Any]       		=							tf.keras.models.load_model(
                            __lowercase	,    custom_objects={main_layer_class.__name__: main_layer_class}				)
                        assert isinstance(__lowercase	,    tf.keras.Model				)
                        lowercase    :				Optional[Any]       		=							model(__lowercase				)
                        self.assert_outputs_same(__lowercase	,    __lowercase				)
   @slow
   def 			__magic_name__			(			self				):
          # make mask reproducible
          np.random.seed(2				)

          lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Optional[Any]       		=							int((config.image_size // config.patch_size) ** 2				)
          lowercase    :				Any       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

          for model_class in self.all_model_classes:
                 lowercase    :				Optional[int]       		=							model_class(__lowercase				)
                 lowercase    :				Union[str, Any]       		=							self._prepare_for_class(__lowercase	,    __lowercase				)
                 lowercase    :				Optional[int]       		=							model(__lowercase	,    noise=__lowercase				)

                 if model_class.__name__ == "TFViTMAEModel":
                        lowercase    :				Any       		=							outputs.last_hidden_state.numpy()
                        lowercase    :				Optional[Any]       		=							0
                 else:
                        lowercase    :				List[str]       		=							outputs.logits.numpy()
                        lowercase    :				Optional[int]       		=							0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(__lowercase	,    saved_model=__lowercase				)
                        lowercase    :				Optional[int]       		=							model_class.from_pretrained(__lowercase				)
                        lowercase    :				List[str]       		=							model(__lowercase	,    noise=__lowercase				)

                        if model_class.__name__ == "TFViTMAEModel":
                               lowercase    :				List[Any]       		=							after_outputs['''last_hidden_state'''].numpy()
                               lowercase    :				List[Any]       		=							0
                        else:
                               lowercase    :				Any       		=							after_outputs['''logits'''].numpy()
                               lowercase    :				Tuple       		=							0

                        lowercase    :				Any       		=							np.amax(np.abs(out_a - out_a				)				)
                        self.assertLessEqual(__lowercase	,    1E-5				)
   def 			__magic_name__			(			self				):
          # make mask reproducible
          np.random.seed(2				)

          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				str       		=							int((config.image_size // config.patch_size) ** 2				)
          lowercase    :				Optional[int]       		=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)

          for model_class in self.all_model_classes:
                 lowercase    :				Tuple       		=							model_class(__lowercase				)
                 lowercase    :				Any       		=							self._prepare_for_class(__lowercase	,    __lowercase				)

                 lowercase    :				Tuple       		=							model(__lowercase	,    noise=__lowercase				)
                 lowercase    :				List[Any]       		=							model.get_config()
                 # make sure that returned config is jsonifiable, which is required by keras
                 json.dumps(__lowercase				)
                 lowercase    :				Optional[Any]       		=							model_class.from_config(model.get_config()				)
                 # make sure it also accepts a normal config
                 lowercase    :				Any       		=							model_class.from_config(model.config				)
                 lowercase    :				List[Any]       		=							new_model(__lowercase				)  # Build model
                 new_model.set_weights(model.get_weights()				)
                 lowercase    :				str       		=							new_model(__lowercase	,    noise=__lowercase				)

                 self.assert_outputs_same(__lowercase	,    __lowercase				)
   @unittest.skip(
       reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"				)
   def 			__magic_name__			(			self				):
          pass

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"				)
          self.assertIsNotNone(__lowercase				)


def   __magic_name__	(      ) ->				Tuple:
       lowercase    :				Tuple       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_tf
@require_vision
class 		a__						(      unittest.TestCase						):
   @cached_property
   def 			__magic_name__			(			self				):
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"				) if is_vision_available() else None

   @slow
   def 			__magic_name__			(			self				):
          # make random mask reproducible across the PT and TF model
          np.random.seed(2				)

          lowercase    :				Optional[Any]       		=							TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"				)

          lowercase    :				Optional[int]       		=							self.default_image_processor
          lowercase    :				Optional[int]       		=							prepare_img()
          lowercase    :				Optional[int]       		=							image_processor(images=__lowercase	,    return_tensors="tf"				)

          # 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)
          lowercase    :				Union[str, Any]       		=							ViTMAEConfig()
          lowercase    :				Union[str, Any]       		=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
          lowercase    :				Tuple       		=							np.random.uniform(size=(1, num_patches)				)

          # forward pass
          lowercase    :				int       		=							model(**__lowercase	,    noise=__lowercase				)

          # verify the logits
          lowercase    :				Optional[int]       		=							tf.convert_to_tensor([1, 196, 768]				)
          self.assertEqual(outputs.logits.shape	,    __lowercase				)

          lowercase    :				List[Any]       		=							tf.convert_to_tensor(
              [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]				)

          tf.debugging.assert_near(outputs.logits[0, :3, :3]	,    __lowercase	,    atol=1E-4				)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





