import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE										=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

_SCREAMING_SNAKE_CASE										=						{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

_SCREAMING_SNAKE_CASE										=						{
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def  snake_case  (       )						->   Tuple:
       _A        =  (
           list(range(ord("""!""")   ,	ord("""~""") + 1)) + list(range(ord("""¡""")   ,	ord("""¬""") + 1)) + list(range(ord("""®""")   ,	ord("""ÿ""") + 1))
       )
       _A        =  bs[:]
       _A        =  0
       for b in range(2**8):
              if b not in bs:
                     bs.append(snake_case__)
                     cs.append(2**8 + n)
                     n += 1
       _A        =  [chr(snake_case__) for n in cs]
       return dict(zip(snake_case__   ,	snake_case__))






def  snake_case  (       snake_case__			:List[Any])						->   int:
       _A        =  set()
       _A        =  word[0]
       for char in word[1:]:
              pairs.add((prev_char, char))
              _A        =  char
       return pairs







class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:str    			=		VOCAB_FILES_NAMES
 lowerCamelCase	:int    			=		PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase	:Union[str, Any]    			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase	:Union[str, Any]    			=		['''input_ids''', '''attention_mask''']



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_="replace"		,				lowerCAmelCase_="<s>"		,				lowerCAmelCase_="</s>"		,				lowerCAmelCase_="</s>"		,				lowerCAmelCase_="<s>"		,				lowerCAmelCase_="<unk>"		,				lowerCAmelCase_="<pad>"		,				lowerCAmelCase_="<mask>"		,				lowerCAmelCase_=False		,				**lowerCAmelCase_		,				)  ->					Optional[Any]:
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else bos_token
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else eos_token
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else sep_token
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else cls_token
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else unk_token
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _A        =  AddedToken(lowerCAmelCase_		,				lstrip=lowerCAmelCase_		,				rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_		,				lowerCAmelCase_							) else mask_token

        super().__init__(
            errors=lowerCAmelCase_		,				bos_token=lowerCAmelCase_		,				eos_token=lowerCAmelCase_		,				unk_token=lowerCAmelCase_		,				sep_token=lowerCAmelCase_		,				cls_token=lowerCAmelCase_		,				pad_token=lowerCAmelCase_		,				mask_token=lowerCAmelCase_		,				add_prefix_space=lowerCAmelCase_		,				**lowerCAmelCase_		,				)

        with open(lowerCAmelCase_		,				encoding="""utf-8"""							) as vocab_handle:
               _A        =  json.load(lowerCAmelCase_							)
        _A        =  {v: k for k, v in self.encoder.items()}
        _A        =  errors  # how to handle errors in decoding
        _A        =  bytes_to_unicode()
        _A        =  {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCAmelCase_		,				encoding="""utf-8"""							) as merges_handle:
               _A        =  merges_handle.read().split("""\n"""							)[1:-1]
        _A        =  [tuple(merge.split()							) for merge in bpe_merges]
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)
        _A        =  {}
        _A        =  add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _A        =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Any:
        return len(self.encoder							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return dict(self.encoder		,				**self.added_tokens_encoder							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if token in self.cache:
               return self.cache[token]
        _A        =  tuple(lowerCAmelCase_							)
        _A        =  get_pairs(lowerCAmelCase_							)

        if not pairs:
               return token

        while True:
               _A        =  min(lowerCAmelCase_		,				key=lambda lowerCAmelCase_							: self.bpe_ranks.get(lowerCAmelCase_		,				float("""inf"""							)							)							)
               if bigram not in self.bpe_ranks:
                      break
               _A   ,  _A        =  bigram
               _A        =  []
               _A        =  0
               while i < len(lowerCAmelCase_							):
                      try:
                             _A        =  word.index(lowerCAmelCase_		,				lowerCAmelCase_							)
                      except ValueError:
                             new_word.extend(word[i:]							)
                             break
                      else:
                             new_word.extend(word[i:j]							)
                             _A        =  j

                      if word[i] == first and i < len(lowerCAmelCase_							) - 1 and word[i + 1] == second:
                             new_word.append(first + second							)
                             i += 2
                      else:
                             new_word.append(word[i]							)
                             i += 1
               _A        =  tuple(lowerCAmelCase_							)
               _A        =  new_word
               if len(lowerCAmelCase_							) == 1:
                      break
               else:
                      _A        =  get_pairs(lowerCAmelCase_							)
        _A        =  """ """.join(lowerCAmelCase_							)
        _A        =  word
        return word



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  []
        for token in re.findall(self.pat		,				lowerCAmelCase_							):
               _A        =  """""".join(
                   self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_							).split(""" """							)							)
        return bpe_tokens



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Tuple:
        return self.encoder.get(lowerCAmelCase_		,				self.encoder.get(self.unk_token							)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Any:
        return self.decoder.get(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Any:
        _A        =  """""".join(lowerCAmelCase_							)
        _A        =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""		,				errors=self.errors							)
        return text



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					Tuple[str]:
        if not os.path.isdir(lowerCAmelCase_							):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
               return
        _A        =  os.path.join(
            lowerCAmelCase_		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        _A        =  os.path.join(
            lowerCAmelCase_		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

        with open(lowerCAmelCase_		,				"""w"""		,				encoding="""utf-8"""							) as f:
               f.write(json.dumps(self.encoder		,				indent=2		,				sort_keys=lowerCAmelCase_		,				ensure_ascii=lowerCAmelCase_							) + """\n"""							)

        _A        =  0
        with open(lowerCAmelCase_		,				"""w"""		,				encoding="""utf-8"""							) as writer:
               writer.write("""#version: 0.2\n"""							)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,				key=lambda lowerCAmelCase_							: kv[1]							):
                      if index != token_index:
                             logger.warning(
                                 F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                 """ Please check that the tokenizer is not corrupted!"""							)
                             _A        =  token_index
                      writer.write(""" """.join(lowerCAmelCase_							) + """\n"""							)
                      index += 1

        return vocab_file, merge_file



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _A        =  [self.cls_token_id]
        _A        =  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = False							)  ->					List[int]:
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase_		,				token_ids_a=lowerCAmelCase_		,				already_has_special_tokens=lowerCAmelCase_							)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase_							)) + [1]
        return [1] + ([0] * len(lowerCAmelCase_							)) + [1, 1] + ([0] * len(lowerCAmelCase_							)) + [1]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None							)  ->					List[int]:
        _A        =  [self.sep_token_id]
        _A        =  [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 + sep + token_ids_a + sep							) * [0]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=False		,				**lowerCAmelCase_							)  ->					Dict:
        _A        =  kwargs.pop("""add_prefix_space"""		,				self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase_							) > 0 and not text[0].isspace()):
               _A        =  """ """ + text
        return (text, kwargs)





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		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=30		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				lowerCAmelCase_=0.6		,				lowerCAmelCase_=None		,				)  ->					int:
        _A        =  parent
        _A        =  batch_size
        _A        =  image_size
        _A        =  patch_size
        _A        =  num_channels
        _A        =  is_training
        _A        =  use_labels
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  type_sequence_label_size
        _A        =  initializer_range
        _A        =  mask_ratio
        _A        =  scope

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



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

        _A        =  self.get_config()

        return config, pixel_values, labels



 def      UpperCAmelCase							(					self							)  ->					str:
        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=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				mask_ratio=self.mask_ratio		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  ViTMAEModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        _A        =  (self.image_size // self.patch_size) ** 2
        _A        =  self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)

        # test greyscale images
        _A        =  1
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        _A        =  model(lowerCAmelCase_							)
        _A        =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 lowerCamelCase	:List[Any]    			=		{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 lowerCamelCase	:List[Any]    			=		False
 lowerCamelCase	:Tuple    			=		False
 lowerCamelCase	:int    			=		False
 lowerCamelCase	:Any    			=		False



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  ViTMAEModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					str:
        self.config_tester.run_common_tests()



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



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)							)
               _A        =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase_		,				nn.Linear							)							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

               _A        =  ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        # make masks reproducible
        np.random.seed(2							)

        _A        =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
        _A        =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
        _A        =  torch.from_numpy(lowerCAmelCase_							)

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

        super().check_pt_tf_models(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2							)
               with torch.no_grad():
                      _A        =  model(**self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)							)

               _A        =  outputs[0].cpu().numpy()
               _A        =  0

               with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(lowerCAmelCase_							)
                      _A        =  model_class.from_pretrained(lowerCAmelCase_							)
                      model.to(lowerCAmelCase_							)
                      # make random mask reproducible
                      torch.manual_seed(2							)
                      with torch.no_grad():
                             _A        =  model(**self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)							)

                      # Make sure we don't have nans
                      _A        =  after_outputs[0].cpu().numpy()
                      _A        =  0
                      _A        =  np.amax(np.abs(out_a - out_a							)							)
                      self.assertLessEqual(lowerCAmelCase_		,				1E-5							)



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



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



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



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



 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  ViTMAEModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






def  snake_case  (       )						->   List[str]:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







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







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					int:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None



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

        _A        =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # 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)
        _A        =  ViTMAEConfig()
        _A        =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
        _A        =  np.random.uniform(size=(1, num_patches)							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_		,				noise=torch.from_numpy(lowerCAmelCase_							).to(device=lowerCAmelCase_							)							)

        # verify the logits
        _A        =  torch.Size((1, 1_96, 7_68)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase_							)

        _A        =  torch.tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

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




