





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 					__snake_case	:


    '''simple docstring'''







    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_=0.02   ,				A_=3   ,				A_=0.6   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			image_size
        SCREAMING_SNAKE_CASE__											=			patch_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			type_sequence_label_size
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			mask_ratio
        SCREAMING_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)
        SCREAMING_SNAKE_CASE__											=			(image_size // patch_size) ** 2
        SCREAMING_SNAKE_CASE__											=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        return config, pixel_values, labels



    def  lowercase_					(       self     ):





        '''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=_lowercase   ,				initializer_range=self.initializer_range   ,				mask_ratio=self.mask_ratio   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFViTMAEForPreTraining(_lowercase     )
        SCREAMING_SNAKE_CASE__											=			model(_lowercase   ,				training=_lowercase     )
        # expected sequence length = num_patches
        SCREAMING_SNAKE_CASE__											=			(self.image_size // self.patch_size) ** 2
        SCREAMING_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
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			TFViTMAEForPreTraining(_lowercase     )

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




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        (SCREAMING_SNAKE_CASE__)											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_tf
class 					__snake_case	(  __a			,     __a			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    lowerCamelCase__    :		int				=	{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

    lowerCamelCase__    :		Union[str, Any]				=	False
    lowerCamelCase__    :		Any				=	False
    lowerCamelCase__    :		Any				=	False
    lowerCamelCase__    :		Optional[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFViTMAEModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(self   ,				config_class=_lowercase   ,				has_text_modality=_lowercase   ,				hidden_size=37     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__											=			['''pixel_values''']
            self.assertListEqual(arg_names[:1]   ,				_lowercase     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowercase     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_lowercase     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(_lowercase     )
            SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(_lowercase   ,				_lowercase     )

            SCREAMING_SNAKE_CASE__											=			model(_lowercase   ,				noise=_lowercase     )

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

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

            return inputs_np_dict

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(_lowercase     )

            SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(_lowercase   ,				_lowercase     )
            SCREAMING_SNAKE_CASE__											=			prepare_numpy_arrays(_lowercase     )

            SCREAMING_SNAKE_CASE__											=			model(_lowercase   ,				noise=_lowercase     )
            SCREAMING_SNAKE_CASE__											=			model(**_lowercase   ,				noise=_lowercase     )
            self.assert_outputs_same(_lowercase   ,				_lowercase     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        np.random.seed(2     )

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

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

        super().check_pt_tf_models(_lowercase   ,				_lowercase   ,				_lowercase     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

        SCREAMING_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(_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     )
        }

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

        for main_layer_class in tf_main_layer_classes:
            SCREAMING_SNAKE_CASE__											=			main_layer_class(_lowercase     )

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

            SCREAMING_SNAKE_CASE__											=			tf.keras.Model(_lowercase   ,				outputs=main_layer(_lowercase     )     )
            SCREAMING_SNAKE_CASE__											=			model(_lowercase     )

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



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(_lowercase     )
            SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(_lowercase   ,				_lowercase     )
            SCREAMING_SNAKE_CASE__											=			model(_lowercase   ,				noise=_lowercase     )

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

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

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

                SCREAMING_SNAKE_CASE__											=			np.amax(np.abs(out_a - out_a     )     )
                self.assertLessEqual(_lowercase   ,				1E-5     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(_lowercase     )
            SCREAMING_SNAKE_CASE__											=			self._prepare_for_class(_lowercase   ,				_lowercase     )

            SCREAMING_SNAKE_CASE__											=			model(_lowercase   ,				noise=_lowercase     )
            SCREAMING_SNAKE_CASE__											=			model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(_lowercase     )
            SCREAMING_SNAKE_CASE__											=			model_class.from_config(model.get_config()     )
            # make sure it also accepts a normal config
            SCREAMING_SNAKE_CASE__											=			model_class.from_config(model.config     )
            SCREAMING_SNAKE_CASE__											=			new_model(_lowercase     )  # Build model
            new_model.set_weights(model.get_weights()     )
            SCREAMING_SNAKE_CASE__											=			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
    to get deterministic results.'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



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





        '''simple docstring'''

        pass




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''     )
        self.assertIsNotNone(_lowercase     )





def 							__snake_case							(    )				->						Dict:
    SCREAMING_SNAKE_CASE__											=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image



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


    '''simple docstring'''







    @cached_property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        np.random.seed(2     )

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

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

        # forward pass
        SCREAMING_SNAKE_CASE__											=			model(**_lowercase   ,				noise=_lowercase     )

        # verify the logits
        SCREAMING_SNAKE_CASE__											=			tf.convert_to_tensor([1, 1_96, 7_68]     )
        self.assertEqual(outputs.logits.shape   ,				_lowercase     )

        SCREAMING_SNAKE_CASE__											=			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]   ,				_lowercase   ,				atol=1E-4     )

from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()