


import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTMAEForPreTraining, ViTMAEModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 			A_   :




      '''simple docstring'''


      def __init__(							self:			List[Any]		, a:			List[Any]		, a:			Union[str, Any]=13		, a:			str=30		, a:			List[str]=2		, a:			Optional[int]=3		, a:			Tuple=True		, a:			List[str]=True		, a:			Tuple=32		, a:			int=5		, a:			int=4		, a:			Tuple=37		, a:			str="gelu"		, a:			str=0.1		, a:			Tuple=0.1		, a:			List[str]=10		, a:			Union[str, Any]=0.0_2		, a:			Any=3		, a:			str=0.6		, a:			Union[str, Any]=None		, ):
       __lowerCamelCase						:				Optional[Any]						   =		parent
       __lowerCamelCase						:				List[str]						   =		batch_size
       __lowerCamelCase						:				Any						   =		image_size
       __lowerCamelCase						:				Any						   =		patch_size
       __lowerCamelCase						:				int						   =		num_channels
       __lowerCamelCase						:				Optional[Any]						   =		is_training
       __lowerCamelCase						:				int						   =		use_labels
       __lowerCamelCase						:				List[Any]						   =		hidden_size
       __lowerCamelCase						:				Optional[Any]						   =		num_hidden_layers
       __lowerCamelCase						:				Optional[Any]						   =		num_attention_heads
       __lowerCamelCase						:				str						   =		intermediate_size
       __lowerCamelCase						:				Optional[int]						   =		hidden_act
       __lowerCamelCase						:				Any						   =		hidden_dropout_prob
       __lowerCamelCase						:				int						   =		attention_probs_dropout_prob
       __lowerCamelCase						:				str						   =		type_sequence_label_size
       __lowerCamelCase						:				int						   =		initializer_range
       __lowerCamelCase						:				Any						   =		mask_ratio
       __lowerCamelCase						:				List[str]						   =		scope

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




      def 	_snake_case      (							self:			str							):
       __lowerCamelCase						:				List[str]						   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       __lowerCamelCase						:				int						   =		None
       if self.use_labels:
        __lowerCamelCase						:				Dict						   =		ids_tensor([self.batch_size]		, self.type_sequence_label_size							)

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

       return config, pixel_values, labels




      def 	_snake_case      (							self:			List[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		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, is_decoder=__snake_case		, initializer_range=self.initializer_range		, mask_ratio=self.mask_ratio		, )




      def 	_snake_case      (							self:			List[Any]		, a:			Optional[Any]		, a:			Dict		, a:			Dict							):
       __lowerCamelCase						:				List[str]						   =		ViTMAEModel(config=__snake_case							)
       model.to(__snake_case							)
       model.eval()
       __lowerCamelCase						:				Dict						   =		model(__snake_case							)
       self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)							)




      def 	_snake_case      (							self:			Optional[Any]		, a:			List[str]		, a:			Union[str, Any]		, a:			Tuple							):
       __lowerCamelCase						:				Union[str, Any]						   =		ViTMAEForPreTraining(__snake_case							)
       model.to(__snake_case							)
       model.eval()
       __lowerCamelCase						:				Union[str, Any]						   =		model(__snake_case							)
       __lowerCamelCase						:				Optional[int]						   =		(self.image_size // self.patch_size) ** 2
       __lowerCamelCase						:				Any						   =		self.patch_size**2 * self.num_channels
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, num_patches, expected_num_channels)							)

       # test greyscale images
       __lowerCamelCase						:				Optional[int]						   =		1
       __lowerCamelCase						:				Optional[Any]						   =		ViTMAEForPreTraining(__snake_case							)
       model.to(__snake_case							)
       model.eval()
       __lowerCamelCase						:				Optional[Any]						   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       __lowerCamelCase						:				str						   =		model(__snake_case							)
       __lowerCamelCase						:				Tuple						   =		self.patch_size**2
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, num_patches, expected_num_channels)							)




      def 	_snake_case      (							self:			Union[str, Any]							):
       __lowerCamelCase						:				List[Any]						   =		self.prepare_config_and_inputs()
       __lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase						:				Optional[Any]						   =		config_and_inputs
       __lowerCamelCase						:				str						   =		{'pixel_values': pixel_values}
       return config, inputs_dict



@require_torch
class 			A_   (			A__   , A__   , unittest.TestCase       ):




      '''simple docstring'''


      __snake_case   			=  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      __snake_case   			=  {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

      __snake_case   			=  False
      __snake_case   			=  False
      __snake_case   			=  False
      __snake_case   			=  False




      def 	_snake_case      (							self:			List[str]							):
       __lowerCamelCase						:				List[str]						   =		ViTMAEModelTester(self							)
       __lowerCamelCase						:				List[str]						   =		ConfigTester(self		, config_class=__snake_case		, has_text_modality=__snake_case		, hidden_size=37							)




      def 	_snake_case      (							self:			List[Any]							):
       self.config_tester.run_common_tests()




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




      def 	_snake_case      (							self:			Union[str, Any]							):
       __lowerCamelCase   ,	__lowerCamelCase						:				List[str]						   =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase						:				Any						   =		model_class(__snake_case							)
        self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)							)
        __lowerCamelCase						:				Union[str, Any]						   =		model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__snake_case		, nn.Linear							)							)




      def 	_snake_case      (							self:			Optional[Any]							):
       __lowerCamelCase   ,	__lowerCamelCase						:				int						   =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase						:				Optional[int]						   =		model_class(__snake_case							)
        __lowerCamelCase						:				List[str]						   =		inspect.signature(model.forward							)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCamelCase						:				Any						   =		[*signature.parameters.keys()]

        __lowerCamelCase						:				Tuple						   =		['pixel_values']
        self.assertListEqual(arg_names[:1]		, __snake_case							)




      def 	_snake_case      (							self:			int							):
       __lowerCamelCase						:				Optional[int]						   =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__snake_case							)




      def 	_snake_case      (							self:			Optional[Any]							):
       __lowerCamelCase						:				Any						   =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__snake_case							)




      def 	_snake_case      (							self:			Dict		, a:			str		, a:			Optional[Any]		, a:			Optional[Any]							):
       # make masks reproducible
       np.random.seed(2							)

       __lowerCamelCase						:				Union[str, Any]						   =		int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
       __lowerCamelCase						:				Union[str, Any]						   =		np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
       __lowerCamelCase						:				Union[str, Any]						   =		torch.from_numpy(__snake_case							)

       # Add `noise` argument.
       # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
       __lowerCamelCase						:				Optional[Any]						   =		pt_noise

       super().check_pt_tf_models(__snake_case		, __snake_case		, __snake_case							)




      def 	_snake_case      (							self:			int							):
       __lowerCamelCase   ,	__lowerCamelCase						:				int						   =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase						:				str						   =		model_class(__snake_case							)
        model.to(__snake_case							)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2							)
        with torch.no_grad():
         __lowerCamelCase						:				List[str]						   =		model(**self._prepare_for_class(__snake_case		, __snake_case							)							)

        __lowerCamelCase						:				Optional[int]						   =		outputs[0].cpu().numpy()
        __lowerCamelCase						:				str						   =		0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(__snake_case							)
         __lowerCamelCase						:				Union[str, Any]						   =		model_class.from_pretrained(__snake_case							)
         model.to(__snake_case							)
         # make random mask reproducible
         torch.manual_seed(2							)
         with torch.no_grad():
          __lowerCamelCase						:				Union[str, Any]						   =		model(**self._prepare_for_class(__snake_case		, __snake_case							)							)

         # Make sure we don't have nans
         __lowerCamelCase						:				List[Any]						   =		after_outputs[0].cpu().numpy()
         __lowerCamelCase						:				Tuple						   =		0
         __lowerCamelCase						:				Union[str, Any]						   =		np.amax(np.abs(out_a - out_a							)							)
         self.assertLessEqual(__snake_case		, 1e-5							)




      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 	_snake_case      (							self:			Optional[Any]							):
       pass




      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 	_snake_case      (							self:			int							):
       pass




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




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




      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 	_snake_case      (							self:			Tuple							):
       pass




      @slow
      def 	_snake_case      (							self:			Any							):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase						:				str						   =		ViTMAEModel.from_pretrained(__snake_case							)
        self.assertIsNotNone(__snake_case							)



def  UpperCamelCase__	(    ):
 __lowerCamelCase						:				Dict						   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image



@require_torch
@require_vision
class 			A_   (			unittest.TestCase       ):




      '''simple docstring'''


      @cached_property
      def 	_snake_case      (							self:			Dict							):
       return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'							) if is_vision_available() else None




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

       __lowerCamelCase						:				str						   =		ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							).to(__snake_case							)

       __lowerCamelCase						:				List[str]						   =		self.default_image_processor
       __lowerCamelCase						:				Optional[int]						   =		prepare_img()
       __lowerCamelCase						:				Any						   =		image_processor(images=__snake_case		, return_tensors='pt'							).to(__snake_case							)

       # 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)
       __lowerCamelCase						:				int						   =		ViTMAEConfig()
       __lowerCamelCase						:				Dict						   =		int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
       __lowerCamelCase						:				Optional[int]						   =		np.random.uniform(size=(1, num_patches)							)

       # forward pass
       with torch.no_grad():
        __lowerCamelCase						:				Union[str, Any]						   =		model(**__snake_case		, noise=torch.from_numpy(__snake_case							).to(device=__snake_case							)							)

       # verify the logits
       __lowerCamelCase						:				int						   =		torch.Size((1, 196, 768)							)
       self.assertEqual(outputs.logits.shape		, __snake_case							)

       __lowerCamelCase						:				int						   =		torch.tensor(
           [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]							)

       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		, expected_slice.to(__snake_case							)		, atol=1e-4							)							)








'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_rembert import RemBertTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None

_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							     =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							     =			{
    "google/rembert": 256,
}

_SCREAMING_SNAKE_CASE							     =			"▁"
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   RemBertTokenizer



     def __init__(   self							:			List[Any]   ,       __snake_case							:			int=None   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Tuple=True   ,       __snake_case							:			Dict=True   ,       __snake_case							:			str=False   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			Optional[int]="[SEP]"   ,       __snake_case							:			str="<unk>"   ,       __snake_case							:			Dict="[SEP]"   ,       __snake_case							:			Dict="<pad>"   ,       __snake_case							:			Union[str, Any]="[CLS]"   ,       __snake_case							:			int="[MASK]"   ,       **__snake_case							:			Optional[int]   ,       )->	Tuple:
        # Mask token behave like a normal word, i.e. include the space before it
        snake_case 						=   AddedToken(__snake_case   ,       lstrip=__snake_case   ,       rstrip=__snake_case						) if isinstance(__snake_case   ,       __snake_case						) else mask_token

        super().__init__(
            __snake_case   ,       tokenizer_file=__snake_case   ,       do_lower_case=__snake_case   ,       remove_space=__snake_case   ,       keep_accents=__snake_case   ,       bos_token=__snake_case   ,       eos_token=__snake_case   ,       unk_token=__snake_case   ,       sep_token=__snake_case   ,       pad_token=__snake_case   ,       cls_token=__snake_case   ,       mask_token=__snake_case   ,       **__snake_case   ,       )

        snake_case 						=   do_lower_case
        snake_case 						=   remove_space
        snake_case 						=   keep_accents
        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None   ,       __snake_case							:			bool = False						)->	List[int]:

        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  """You should not supply a second sequence if the provided sequence of """
                  """ids is already formatted with special tokens for the model."""						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]
        return [1] + ([0] * len(__snake_case						)) + [1]



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not os.path.isdir(__snake_case						):
           logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case						)						)
           return
        snake_case 						=   os.path.join(
            __snake_case   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(__snake_case						):
           copyfile(self.vocab_file   ,       __snake_case						)

        return (out_vocab_file,)

