

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  ,   lowercase__  ,   lowercase__=13  ,   lowercase__=30  ,   lowercase__=2  ,   lowercase__=3  ,   lowercase__=True  ,   lowercase__=True  ,   lowercase__=32  ,   lowercase__=2  ,   lowercase__=4  ,   lowercase__=37  ,   lowercase__="gelu"  ,   lowercase__=0.1  ,   lowercase__=0.1  ,   lowercase__=10  ,   lowercase__=0.02  ,   lowercase__=3  ,   lowercase__=0.6  ,   lowercase__=None  ,   )  ->   Optional[Any]:
        __UpperCAmelCase					       =      parent
        __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					       =      mask_ratio
        __UpperCAmelCase					       =      scope

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


    def   lowerCAmelCase_    (self	)  ->   int:
        __UpperCAmelCase					       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

        __UpperCAmelCase					       =      self.get_config()

        return config, pixel_values, labels


    def   lowerCAmelCase_    (self	)  ->   Union[str, 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  ,   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=A_  ,   initializer_range=self.initializer_range  ,   mask_ratio=self.mask_ratio  ,   )


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   Optional[int]:
        __UpperCAmelCase					       =      TFViTMAEModel(config=A_	)
        __UpperCAmelCase					       =      model(A_  ,   training=A_	)
        self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, self.seq_length, self.hidden_size)	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   int:
        __UpperCAmelCase					       =      TFViTMAEForPreTraining(A_	)
        __UpperCAmelCase					       =      model(A_  ,   training=A_	)
        # expected sequence length = num_patches
        __UpperCAmelCase					       =      (self.image_size // self.patch_size) ** 2
        __UpperCAmelCase					       =      self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, num_patches, expected_num_channels)	)

        # test greyscale images
        __UpperCAmelCase					       =      1
        __UpperCAmelCase					       =      TFViTMAEForPreTraining(A_	)

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






    def   lowerCAmelCase_    (self	)  ->   List[str]:
        __UpperCAmelCase					       =      self.prepare_config_and_inputs()
        ((__UpperCAmelCase)       ,  (__UpperCAmelCase)       ,  (__UpperCAmelCase))					       =      config_and_inputs
        __UpperCAmelCase					       =      {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_tf
class        A_ (   _a			,  _a			,  unittest.TestCase    ):


    '''simple docstring'''






    a__  				=							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    a__  				=							{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

    a__  				=							False
    a__  				=							False
    a__  				=							False
    a__  				=							False


    def   lowerCAmelCase_    (self	)  ->   List[str]:
        __UpperCAmelCase					       =      TFViTMAEModelTester(self	)
        __UpperCAmelCase					       =      ConfigTester(self  ,   config_class=A_  ,   has_text_modality=A_  ,   hidden_size=37	)


    def   lowerCAmelCase_    (self	)  ->   int:
        self.config_tester.run_common_tests()


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


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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


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

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

            __UpperCAmelCase					       =      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]  ,   A_	)


    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_	)


    def   lowerCAmelCase_    (self	)  ->   int:
        __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*A_	)


    def   lowerCAmelCase_    (self	)  ->   str:
        # make the mask reproducible
        np.random.seed(2	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      model_class(A_	)
            __UpperCAmelCase					       =      self._prepare_for_class(A_  ,   A_	)

            __UpperCAmelCase					       =      model(A_  ,   noise=A_	)

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

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


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        # make the mask reproducible
        np.random.seed(2	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

        def prepare_numpy_arrays(lowercase__	):
            __UpperCAmelCase					       =      {}
            for k, v in inputs_dict.items():
                if tf.is_tensor(A_	):
                    __UpperCAmelCase					       =      v.numpy()
                else:
                    __UpperCAmelCase					       =      np.array(A_	)

            return inputs_np_dict

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      model_class(A_	)

            __UpperCAmelCase					       =      self._prepare_for_class(A_  ,   A_	)
            __UpperCAmelCase					       =      prepare_numpy_arrays(A_	)

            __UpperCAmelCase					       =      model(A_  ,   noise=A_	)
            __UpperCAmelCase					       =      model(**A_  ,   noise=A_	)
            self.assert_outputs_same(A_  ,   A_	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__	)  ->   Optional[int]:
        # make masks reproducible
        np.random.seed(2	)

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

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

        super().check_pt_tf_models(A_  ,   A_  ,   A_	)


    def   lowerCAmelCase_    (self	)  ->   int:
        # make mask reproducible
        np.random.seed(2	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

        for main_layer_class in tf_main_layer_classes:
            __UpperCAmelCase					       =      main_layer_class(A_	)

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

            __UpperCAmelCase					       =      tf.keras.Model(A_  ,   outputs=main_layer(A_	)	)
            __UpperCAmelCase					       =      model(A_	)

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


    @slow
    def   lowerCAmelCase_    (self	)  ->   Any:
        # make mask reproducible
        np.random.seed(2	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      model_class(A_	)
            __UpperCAmelCase					       =      self._prepare_for_class(A_  ,   A_	)
            __UpperCAmelCase					       =      model(A_  ,   noise=A_	)

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

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

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

                __UpperCAmelCase					       =      np.amax(np.abs(out_a - out_a	)	)
                self.assertLessEqual(A_  ,   1E-5	)


    def   lowerCAmelCase_    (self	)  ->   Optional[Any]:
        # make mask reproducible
        np.random.seed(2	)

        __UpperCAmelCase       ,  __UpperCAmelCase					       =      self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            __UpperCAmelCase					       =      model_class(A_	)
            __UpperCAmelCase					       =      self._prepare_for_class(A_  ,   A_	)

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

            self.assert_outputs_same(A_  ,   A_	)


    @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	)  ->   List[Any]:
        pass


    @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''	)
    def   lowerCAmelCase_    (self	)  ->   Any:
        pass






    @slow
    def   lowerCAmelCase_    (self	)  ->   Tuple:
        __UpperCAmelCase					       =      TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''	)
        self.assertIsNotNone(A_	)






def    __a							( )   ->  List[Any]:






    '''simple docstring'''
    __UpperCAmelCase					       =      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	)  ->   Optional[int]:
        return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''	) if is_vision_available() else None






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

        __UpperCAmelCase					       =      TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''	)

        __UpperCAmelCase					       =      self.default_image_processor
        __UpperCAmelCase					       =      prepare_img()
        __UpperCAmelCase					       =      image_processor(images=A_  ,   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)
        __UpperCAmelCase					       =      ViTMAEConfig()
        __UpperCAmelCase					       =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
        __UpperCAmelCase					       =      np.random.uniform(size=(1, num_patches)	)

        # forward pass
        __UpperCAmelCase					       =      model(**A_  ,   noise=A_	)

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

        __UpperCAmelCase					       =      tf.convert_to_tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]	)

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








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__						: str    =				logging.get_logger(__name__)

__magic_name__						: List[Any]    =				{
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''visual_bert'''




							def __init__(     self:    Any     ,							A_:    List[str]=3_05_22     ,							A_:    Tuple=7_68     ,							A_:    Dict=5_12     ,							A_:    List[Any]=12     ,							A_:    str=12     ,							A_:    Dict=30_72     ,							A_:    str="gelu"     ,							A_:    Union[str, Any]=0.1     ,							A_:    Any=0.1     ,							A_:    List[Any]=5_12     ,							A_:    int=2     ,							A_:    int=0.02     ,							A_:    Any=1E-12     ,							A_:    List[str]=False     ,							A_:    str=True     ,							A_:    Union[str, Any]=1     ,							A_:    Optional[int]=0     ,							A_:    Dict=2     ,							**A_:    Optional[int]     ,							):
										super().__init__(pad_token_id=A_     ,							bos_token_id=A_     ,							eos_token_id=A_     ,							**A_   )

										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      visual_embedding_dim
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      layer_norm_eps
										__lowerCamelCase          =      bypass_transformer
										__lowerCamelCase          =      special_visual_initialize




