


"""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   :





							'''simple docstring'''

							def __init__( self							:					Tuple						,				UpperCAmelCase_							:					List[str]						,				UpperCAmelCase_							:					Optional[int]=13						,				UpperCAmelCase_							:					Union[str, Any]=30						,				UpperCAmelCase_							:					Dict=2						,				UpperCAmelCase_							:					Dict=3						,				UpperCAmelCase_							:					Dict=True						,				UpperCAmelCase_							:					Dict=True						,				UpperCAmelCase_							:					Optional[int]=32						,				UpperCAmelCase_							:					Any=2						,				UpperCAmelCase_							:					List[Any]=4						,				UpperCAmelCase_							:					Any=37						,				UpperCAmelCase_							:					Optional[Any]="gelu"						,				UpperCAmelCase_							:					Dict=0.1						,				UpperCAmelCase_							:					List[str]=0.1						,				UpperCAmelCase_							:					int=10						,				UpperCAmelCase_							:					Dict=0.02						,				UpperCAmelCase_							:					Optional[Any]=3						,				UpperCAmelCase_							:					Any=0.6						,				UpperCAmelCase_							:					Any=None						,				)    ->Dict:



														"""simple docstring"""



														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 						lowerCAmelCase				( self							:					Tuple				)    ->Union[str, Any]:



														"""simple docstring"""



														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 						lowerCAmelCase				( self							:					Tuple				)    ->Union[str, Any]:



														"""simple docstring"""



														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=UpperCAmelCase_						,				initializer_range=self.initializer_range						,				mask_ratio=self.mask_ratio						,				)



							def 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					int				)    ->Dict:



														"""simple docstring"""



														snake_case_							=						TFViTMAEModel(config=UpperCAmelCase_				)
														snake_case_							=						model(UpperCAmelCase_						,				training=UpperCAmelCase_				)
														self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)				)



							def 						lowerCAmelCase				( self							:					Optional[Any]						,				UpperCAmelCase_							:					Any						,				UpperCAmelCase_							:					Tuple						,				UpperCAmelCase_							:					Union[str, Any]				)    ->Optional[int]:



														"""simple docstring"""



														snake_case_							=						TFViTMAEForPreTraining(UpperCAmelCase_				)
														snake_case_							=						model(UpperCAmelCase_						,				training=UpperCAmelCase_				)
														# expected sequence length = num_patches
														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_							=						TFViTMAEForPreTraining(UpperCAmelCase_				)

														snake_case_							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
														snake_case_							=						model(UpperCAmelCase_						,				training=UpperCAmelCase_				)
														snake_case_							=						self.patch_size**2
														self.parent.assertEqual(result.logits.shape						,				(self.batch_size, num_patches, expected_num_channels)				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->Union[str, Any]:



														"""simple docstring"""



														snake_case_							=						self.prepare_config_and_inputs()
														(snake_case_)							=						config_and_inputs
														snake_case_							=						{"""pixel_values""": pixel_values}
														return config, inputs_dict





@require_tf
class    __A   (_SCREAMING_SNAKE_CASE  ,						_SCREAMING_SNAKE_CASE  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Optional[Any]												=			(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							__lowercase:						Optional[Any]												=			{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							__lowercase:						Optional[int]												=			False
							__lowercase:						str												=			False
							__lowercase:						Optional[Any]												=			False
							__lowercase:						Any												=			False



							def 						lowerCAmelCase				( self							:					str				)    ->str:



														"""simple docstring"""



														snake_case_							=						TFViTMAEModelTester(self				)
														snake_case_							=						ConfigTester(self						,				config_class=UpperCAmelCase_						,				has_text_modality=UpperCAmelCase_						,				hidden_size=37				)



							def 						lowerCAmelCase				( self							:					Tuple				)    ->Union[str, Any]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""				)
							def 						lowerCAmelCase				( self							:					int				)    ->Optional[Any]:



														"""simple docstring"""



														pass



							def 						lowerCAmelCase				( self							:					str				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)
																					self.assertIsInstance(model.get_input_embeddings()						,				(tf.keras.layers.Layer)				)
																					snake_case_							=						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCAmelCase_						,				tf.keras.layers.Layer				)				)



							def 						lowerCAmelCase				( self							:					Dict				)    ->Optional[int]:



														"""simple docstring"""



														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)
																					snake_case_							=						inspect.signature(model.call				)
																					# 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]						,				UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					List[str]				)    ->Optional[Any]:



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_							=						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Optional[int]				)    ->Any:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)
																					snake_case_							=						self._prepare_for_class(UpperCAmelCase_						,				UpperCAmelCase_				)

																					snake_case_							=						model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)

																					snake_case_							=						copy.deepcopy(self._prepare_for_class(UpperCAmelCase_						,				UpperCAmelCase_				)				)
																					snake_case_							=						model(**UpperCAmelCase_						,				noise=UpperCAmelCase_				)
																					snake_case_							=						outputs_dict[0].numpy()
																					snake_case_							=						outputs_keywords[0].numpy()

																					self.assertLess(np.sum(np.abs(output_dict - output_keywords				)				)						,				1E-6				)



							def 						lowerCAmelCase				( self							:					List[Any]				)    ->Optional[int]:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

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

														def prepare_numpy_arrays(UpperCAmelCase_							:					Dict				):
																					snake_case_							=						{}
																					for k, v in inputs_dict.items():
																												if tf.is_tensor(UpperCAmelCase_				):
																																			snake_case_							=						v.numpy()
																												else:
																																			snake_case_							=						np.array(UpperCAmelCase_				)

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)

																					snake_case_							=						self._prepare_for_class(UpperCAmelCase_						,				UpperCAmelCase_				)
																					snake_case_							=						prepare_numpy_arrays(UpperCAmelCase_				)

																					snake_case_							=						model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)
																					snake_case_							=						model(**UpperCAmelCase_						,				noise=UpperCAmelCase_				)
																					self.assert_outputs_same(UpperCAmelCase_						,				UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Dict						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					Dict				)    ->Optional[Any]:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						int((tf_model.config.image_size // tf_model.config.patch_size) ** 2				)
														snake_case_							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
														snake_case_							=						tf.constant(UpperCAmelCase_				)

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

														super().check_pt_tf_models(UpperCAmelCase_						,				UpperCAmelCase_						,				UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					int				)    ->Union[str, Any]:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

														snake_case_							=						{
														    module_member
														    for model_class in self.all_model_classes
														    for module in (import_module(model_class.__module__				),)
														    for module_member_name in dir(UpperCAmelCase_				)
														    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(UpperCAmelCase_						,				UpperCAmelCase_				),)
														    if isinstance(UpperCAmelCase_						,				UpperCAmelCase_				)
														    and tf.keras.layers.Layer in module_member.__bases__
														    and getattr(UpperCAmelCase_						,				"""_keras_serializable"""						,				UpperCAmelCase_				)
														}

														snake_case_							=						int((config.image_size // config.patch_size) ** 2				)
														snake_case_							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
														snake_case_							=						tf.convert_to_tensor(UpperCAmelCase_				)
														inputs_dict.update({"""noise""": noise}				)

														for main_layer_class in tf_main_layer_classes:
																					snake_case_							=						main_layer_class(UpperCAmelCase_				)

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

																					snake_case_							=						tf.keras.Model(UpperCAmelCase_						,				outputs=main_layer(UpperCAmelCase_				)				)
																					snake_case_							=						model(UpperCAmelCase_				)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												snake_case_							=						os.path.join(UpperCAmelCase_						,				"""keras_model.h5"""				)
																												model.save(UpperCAmelCase_				)
																												snake_case_							=						tf.keras.models.load_model(
																												    UpperCAmelCase_						,				custom_objects={main_layer_class.__name__: main_layer_class}				)
																												assert isinstance(UpperCAmelCase_						,				tf.keras.Model				)
																												snake_case_							=						model(UpperCAmelCase_				)
																												self.assert_outputs_same(UpperCAmelCase_						,				UpperCAmelCase_				)



							@slow
							def 						lowerCAmelCase				( self							:					List[Any]				)    ->Any:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)
																					snake_case_							=						self._prepare_for_class(UpperCAmelCase_						,				UpperCAmelCase_				)
																					snake_case_							=						model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)

																					if model_class.__name__ == "TFViTMAEModel":
																												snake_case_							=						outputs.last_hidden_state.numpy()
																												snake_case_							=						0
																					else:
																												snake_case_							=						outputs.logits.numpy()
																												snake_case_							=						0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(UpperCAmelCase_						,				saved_model=UpperCAmelCase_				)
																												snake_case_							=						model_class.from_pretrained(UpperCAmelCase_				)
																												snake_case_							=						model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)

																												if model_class.__name__ == "TFViTMAEModel":
																																			snake_case_							=						after_outputs["""last_hidden_state"""].numpy()
																																			snake_case_							=						0
																												else:
																																			snake_case_							=						after_outputs["""logits"""].numpy()
																																			snake_case_							=						0

																												snake_case_							=						np.amax(np.abs(out_a - out_a				)				)
																												self.assertLessEqual(UpperCAmelCase_						,				1E-5				)



							def 						lowerCAmelCase				( self							:					int				)    ->str:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						self.model_tester.prepare_config_and_inputs_for_common()

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

														for model_class in self.all_model_classes:
																					snake_case_							=						model_class(UpperCAmelCase_				)
																					snake_case_							=						self._prepare_for_class(UpperCAmelCase_						,				UpperCAmelCase_				)

																					snake_case_							=						model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)
																					snake_case_							=						model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(UpperCAmelCase_				)
																					snake_case_							=						model_class.from_config(model.get_config()				)
																					# make sure it also accepts a normal config
																					snake_case_							=						model_class.from_config(model.config				)
																					snake_case_							=						new_model(UpperCAmelCase_				)  # Build model
																					new_model.set_weights(model.get_weights()				)
																					snake_case_							=						new_model(UpperCAmelCase_						,				noise=UpperCAmelCase_				)

																					self.assert_outputs_same(UpperCAmelCase_						,				UpperCAmelCase_				)



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



														"""simple docstring"""



														pass



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



														"""simple docstring"""



														pass



							@slow
							def 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->Any:



														"""simple docstring"""



														snake_case_							=						TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""				)
														self.assertIsNotNone(UpperCAmelCase_				)







def   _a (					)			-> Union[str, Any]:
							snake_case_							=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
							return image





@require_tf
@require_vision
class    __A   (unittest.TestCase):





							'''simple docstring'''

							@cached_property
							def 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->List[Any]:



														"""simple docstring"""



														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""				) if is_vision_available() else None



							@slow
							def 						lowerCAmelCase				( self							:					List[Any]				)    ->List[Any]:



														"""simple docstring"""



														np.random.seed(2				)

														snake_case_							=						TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""				)

														snake_case_							=						self.default_image_processor
														snake_case_							=						prepare_img()
														snake_case_							=						image_processor(images=UpperCAmelCase_						,				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)
														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
														snake_case_							=						model(**UpperCAmelCase_						,				noise=UpperCAmelCase_				)

														# verify the logits
														snake_case_							=						tf.convert_to_tensor([1, 196, 768]				)
														self.assertEqual(outputs.logits.shape						,				UpperCAmelCase_				)

														snake_case_							=						tf.convert_to_tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]				)

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







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for data in source_data:
    for i, el in enumerate(A__					):
      if len(A__					) < i + 1:
        data_lists.append([]					)
      data_lists[i].append(float(A__					)					)
  return data_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[list[float]]			=   []
  for dlist, weight in zip(A__							,    A__					):
    lowerCAmelCase_			:      Tuple			=   min(A__					)
    lowerCAmelCase_			:      str			=   max(A__					)

    lowerCAmelCase_			:      list[float]			=   []
    # for weight 0 score is 1 - actual score
    if weight == 0:
      for item in dlist:
        try:
          score.append(1 - ((item - mind) / (maxd - mind))					)
        except ZeroDivisionError:
          score.append(1					)

    elif weight == 1:
      for item in dlist:
        try:
          score.append((item - mind) / (maxd - mind)					)
        except ZeroDivisionError:
          score.append(0					)

        # weight not 0 or 1
    else:
      lowerCAmelCase_			:      List[Any]			=   f'Invalid weight of {weight:f} provided'
      raise ValueError(A__					)

    score_lists.append(A__					)

  return score_lists







def 				UpperCamelCase_			(    A__					:   list[list[float]]					):
  '''simple docstring'''





  lowerCAmelCase_			:      list[float]			=   [0 for i in range(len(score_lists[0]					)					)]

  for slist in score_lists:
    for j, ele in enumerate(A__					):
      lowerCAmelCase_			:      List[Any]			=   final_scores[j] + ele

  return final_scores







def 				UpperCamelCase_			(    A__					:   list[list[float]]							,    A__					:   list[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   get_data(A__					)
  lowerCAmelCase_			:      Tuple			=   calculate_each_score(A__							,    A__					)
  lowerCAmelCase_			:      Optional[int]			=   generate_final_scores(A__					)

  # append scores to source data
  for i, ele in enumerate(A__					):
    source_data[i].append(A__					)

  return source_data



