'''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       UpperCAmelCase__  :






   """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       ,				)    ->     Dict:
        lowercase_					:       str     				=					parent
        lowercase_					:       Optional[Any]     				=					batch_size
        lowercase_					:       int     				=					image_size
        lowercase_					:       Optional[int]     				=					patch_size
        lowercase_					:       Tuple     				=					num_channels
        lowercase_					:       List[str]     				=					is_training
        lowercase_					:       Dict     				=					use_labels
        lowercase_					:       List[str]     				=					hidden_size
        lowercase_					:       Dict     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       int     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					hidden_act
        lowercase_					:       Any     				=					hidden_dropout_prob
        lowercase_					:       Any     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					type_sequence_label_size
        lowercase_					:       Optional[Any]     				=					initializer_range
        lowercase_					:       Dict     				=					mask_ratio
        lowercase_					:       Dict     				=					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[int]     				=					(image_size // patch_size) ** 2
        lowercase_					:       str     				=					int(math.ceil((1 - mask_ratio) * (num_patches + 1)				)				)
   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       int     				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

        lowercase_					:       str     				=					None
        if self.use_labels:
             lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size]       ,				self.type_sequence_label_size				)

        lowercase_					:       int     				=					self.get_config()

        return config, pixel_values, labels
   def 		_lowerCamelCase (self				)    ->     Tuple:
        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       ,				_a       ,				_a       ,				_a				)    ->     Optional[int]:
        lowercase_					:       Optional[int]     				=					TFViTMAEModel(config=_a				)
        lowercase_					:       Optional[int]     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFViTMAEForPreTraining(_a				)
        lowercase_					:       int     				=					model(_a       ,				training=_a				)
        # expected sequence length = num_patches
        lowercase_					:       Any     				=					(self.image_size // self.patch_size) ** 2
        lowercase_					:       Dict     				=					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_					:       int     				=					TFViTMAEForPreTraining(_a				)

        lowercase_					:       Optional[int]     				=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
        lowercase_					:       List[Any]     				=					model(_a       ,				training=_a				)
        lowercase_					:       List[Any]     				=					self.patch_size**2
        self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, num_patches, expected_num_channels)				)





   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Any     				=					self.prepare_config_and_inputs()
        ((lowercase_)				,(lowercase_)				,(lowercase_))					:       int     				=					config_and_inputs
        lowercase_					:       Tuple     				=					{'pixel_values': pixel_values}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Optional[int]     =    (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   A							:       List[str]     =    {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

   A							:       Optional[int]     =    False
   A							:       str     =    False
   A							:       Optional[Any]     =    False
   A							:       Dict     =    False
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Any     				=					TFViTMAEModelTester(self				)
        lowercase_					:       List[str]     				=					ConfigTester(self       ,				config_class=_a       ,				has_text_modality=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViTMAE does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        pass
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       Any     				=					model_class(_a				)
             self.assertIsInstance(model.get_input_embeddings()       ,				(tf.keras.layers.Layer)				)
             lowercase_					:       Any     				=					model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_a       ,				tf.keras.layers.Layer				)				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_					:       List[Any]     				=					model_class(_a				)
             lowercase_					:       Dict     				=					inspect.signature(model.call				)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_					:       Optional[int]     				=					[*signature.parameters.keys()]

             lowercase_					:       str     				=					['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Dict     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        # make the mask reproducible
        np.random.seed(2				)

        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

        lowercase_					:       Optional[int]     				=					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(_a				)
             lowercase_					:       Optional[int]     				=					self._prepare_for_class(_a       ,				_a				)

             lowercase_					:       int     				=					model(_a       ,				noise=_a				)

             lowercase_					:       int     				=					copy.deepcopy(self._prepare_for_class(_a       ,				_a				)				)
             lowercase_					:       int     				=					model(**_a       ,				noise=_a				)
             lowercase_					:       Union[str, Any]     				=					outputs_dict[0].numpy()
             lowercase_					:       Optional[int]     				=					outputs_keywords[0].numpy()

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

        lowercase_				,lowercase_					:       List[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

             return inputs_np_dict

        for model_class in self.all_model_classes:
             lowercase_					:       List[Any]     				=					model_class(_a				)

             lowercase_					:       Optional[int]     				=					self._prepare_for_class(_a       ,				_a				)
             lowercase_					:       Any     				=					prepare_numpy_arrays(_a				)

             lowercase_					:       List[Any]     				=					model(_a       ,				noise=_a				)
             lowercase_					:       Any     				=					model(**_a       ,				noise=_a				)
             self.assert_outputs_same(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Optional[int]:
        # make masks reproducible
        np.random.seed(2				)

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

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

        super().check_pt_tf_models(_a       ,				_a       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        # make mask reproducible
        np.random.seed(2				)

        lowercase_				,lowercase_					:       Any     				=					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(_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				)
        }

        lowercase_					:       Optional[int]     				=					int((config.image_size // config.patch_size) ** 2				)
        lowercase_					:       int     				=					np.random.uniform(size=(self.model_tester.batch_size, num_patches)				)
        lowercase_					:       Any     				=					tf.convert_to_tensor(_a				)
        inputs_dict.update({'noise': noise}				)

        for main_layer_class in tf_main_layer_classes:
             lowercase_					:       Optional[Any]     				=					main_layer_class(_a				)

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

             lowercase_					:       Tuple     				=					tf.keras.Model(_a       ,				outputs=main_layer(_a				)				)
             lowercase_					:       str     				=					model(_a				)

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

        lowercase_				,lowercase_					:       Optional[int]     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
             lowercase_					:       int     				=					model_class(_a				)
             lowercase_					:       Any     				=					self._prepare_for_class(_a       ,				_a				)
             lowercase_					:       Dict     				=					model(_a       ,				noise=_a				)

             if model_class.__name__ == "TFViTMAEModel":
                  lowercase_					:       Tuple     				=					outputs.last_hidden_state.numpy()
                  lowercase_					:       Dict     				=					0
             else:
                  lowercase_					:       List[str]     				=					outputs.logits.numpy()
                  lowercase_					:       Tuple     				=					0

             with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(_a       ,				saved_model=_a				)
                  lowercase_					:       Tuple     				=					model_class.from_pretrained(_a				)
                  lowercase_					:       int     				=					model(_a       ,				noise=_a				)

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

                  lowercase_					:       str     				=					np.amax(np.abs(out_a - out_a				)				)
                  self.assertLessEqual(_a       ,				1e-5				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        # make mask reproducible
        np.random.seed(2				)

        lowercase_				,lowercase_					:       int     				=					self.model_tester.prepare_config_and_inputs_for_common()

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

        for model_class in self.all_model_classes:
             lowercase_					:       Union[str, Any]     				=					model_class(_a				)
             lowercase_					:       Optional[int]     				=					self._prepare_for_class(_a       ,				_a				)

             lowercase_					:       Any     				=					model(_a       ,				noise=_a				)
             lowercase_					:       int     				=					model.get_config()
             # make sure that returned config is jsonifiable, which is required by keras
             json.dumps(_a				)
             lowercase_					:       List[Any]     				=					model_class.from_config(model.get_config()				)
             # make sure it also accepts a normal config
             lowercase_					:       List[str]     				=					model_class.from_config(model.config				)
             lowercase_					:       Tuple     				=					new_model(_a				)  # Build model
             new_model.set_weights(model.get_weights()				)
             lowercase_					:       Any     				=					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\n    to get deterministic results.'				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass
   @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'				)
   def 		_lowerCamelCase (self				)    ->     str:
        pass





   @slow
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       Union[str, Any]     				=					TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'				)
        self.assertIsNotNone(_a				)



def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image






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






   """simple docstring"""
   @cached_property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'				) if is_vision_available() else None





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

        lowercase_					:       int     				=					TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'				)

        lowercase_					:       Optional[int]     				=					self.default_image_processor
        lowercase_					:       Optional[int]     				=					prepare_img()
        lowercase_					:       List[str]     				=					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)
        lowercase_					:       Optional[int]     				=					ViTMAEConfig()
        lowercase_					:       Dict     				=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2				)
        lowercase_					:       Union[str, Any]     				=					np.random.uniform(size=(1, num_patches)				)

        # forward pass
        lowercase_					:       Union[str, Any]     				=					model(**_a       ,				noise=_a				)

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

        lowercase_					:       Tuple     				=					tf.convert_to_tensor(
            [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]				)

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





'''simple docstring'''




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    TransfoXLTokenizer
   A							:       Optional[int]     =    False
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        super().setUp()

        lowercase_					:       Any     				=					[
            '<unk>',
            '[CLS]',
            '[SEP]',
            'want',
            'unwanted',
            'wa',
            'un',
            'running',
            ',',
            'low',
            'l',
        ]
        lowercase_					:       Any     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					'<unk> UNwanted , running'
        lowercase_					:       int     				=					'<unk> unwanted, running'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					TransfoXLTokenizer(vocab_file=self.vocab_file       ,				lower_case=_a				)

        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize('<unk> UNwanted , running'				)
        self.assertListEqual(_a       ,				['<unk>', 'unwanted', ',', 'running']				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				[0, 4, 8, 7]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[str]     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['hello', '!', 'how', 'are', 'you', '?']				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)
        lowercase_					:       int     				=					'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
        lowercase_					:       List[str]     				=					[
            'Hello',
            '(',
            'bracket',
            ')',
            'and',
            'side',
            '@-@',
            'scrolled',
            '[',
            'and',
            ']',
            'Henry',
            '\'s',
            '$',
            '5',
            '@,@',
            '000',
            'with',
            '3',
            '@.@',
            '34',
            'm',
            '.',
            'What',
            '\'s',
            'up',
            '!',
            '?',
        ]

        self.assertListEqual(tokenizer.tokenize(_a				)       ,				_a				)

        self.assertEqual(tokenizer.convert_tokens_to_string(_a				)       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					self.get_tokenizer()
        lowercase_					:       str     				=					len(_a				)

        tokenizer.add_tokens(['new1', 'new2']				)
        tokenizer.move_added_token('new1'       ,				1				)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(_a				)       ,				original_len + 2				)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode('new1'				)       ,				[1]				)
        self.assertEqual(tokenizer.decode([1]				)       ,				'new1'				)




