


'''simple docstring'''






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__A :  List[str]    							=				logging.get_logger(__name__)

__A :  str    							=				"▁"

__A :  Union[str, Any]    							=				{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A :  Any    							=				{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

__A :  List[Any]    							=				{
    "facebook/m2m100_418M": 1024,
}

# fmt: off
__A :  Dict    							=				{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= VOCAB_FILES_NAMES
    lowercase       			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase       			= PRETRAINED_VOCAB_FILES_MAP
    lowercase       			= ['input_ids', 'attention_mask']

    lowercase       			= []
    lowercase       			= []

    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  List[str]=None   ,						lowerCamelCase   :  Dict=None   ,						lowerCamelCase   :  Any="<s>"   ,						lowerCamelCase   :  Optional[int]="</s>"   ,						lowerCamelCase   :  Dict="</s>"   ,						lowerCamelCase   :  Optional[Any]="<pad>"   ,						lowerCamelCase   :  Tuple="<unk>"   ,						lowerCamelCase   :  int="m2m100"   ,						lowerCamelCase   :  Optional[Dict[str, Any]] = None   ,						lowerCamelCase   :  Tuple=8   ,						**lowerCamelCase   :  Any   ,						)       ->  None:
      lowerCAmelCase_      :				Dict      =       {} if sp_model_kwargs is None else sp_model_kwargs

      lowerCAmelCase_      :				Dict      =       language_codes
      lowerCAmelCase_      :				int      =       FAIRSEQ_LANGUAGE_CODES[language_codes]
      lowerCAmelCase_      :				int      =       {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

      lowerCAmelCase_      :				int      =       kwargs.get("""additional_special_tokens"""   ,						[]		)
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(lowerCamelCase		)
          for lang_code in fairseq_language_code
          if self.get_lang_token(lowerCamelCase		) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=lowerCamelCase   ,						tgt_lang=lowerCamelCase   ,						bos_token=lowerCamelCase   ,						eos_token=lowerCamelCase   ,						sep_token=lowerCamelCase   ,						unk_token=lowerCamelCase   ,						pad_token=lowerCamelCase   ,						language_codes=lowerCamelCase   ,						sp_model_kwargs=self.sp_model_kwargs   ,						num_madeup_words=lowerCamelCase   ,						**lowerCamelCase   ,						)

      lowerCAmelCase_      :				List[str]      =       vocab_file
      lowerCAmelCase_      :				Any      =       load_json(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       {v: k for k, v in self.encoder.items()}
      lowerCAmelCase_      :				Optional[int]      =       spm_file
      lowerCAmelCase_      :				Union[str, Any]      =       load_spm(lowerCamelCase   ,						self.sp_model_kwargs		)

      lowerCAmelCase_      :				str      =       len(self.encoder		)

      lowerCAmelCase_      :				str      =       {
          self.get_lang_token(lowerCamelCase		): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase		)
      }
      lowerCAmelCase_      :				Optional[Any]      =       {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase		)}
      lowerCAmelCase_      :				Any      =       {v: k for k, v in self.lang_token_to_id.items()}

      lowerCAmelCase_      :				Union[str, Any]      =       src_lang if src_lang is not None else """en"""
      lowerCAmelCase_      :				int      =       tgt_lang
      lowerCAmelCase_      :				Dict      =       self.get_lang_id(self._src_lang		)
      self.set_src_lang_special_tokens(self._src_lang		)

      lowerCAmelCase_      :				str      =       num_madeup_words

    @property
    def __lowercase       (       self   :  Dict		)       ->  int:
      return len(self.encoder		) + len(self.lang_token_to_id		)

    @property
    def __lowercase       (       self   :  Any		)       ->  str:
      return self._src_lang

    @src_lang.setter
    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str		)       ->  None:
      lowerCAmelCase_      :				Any      =       new_src_lang
      self.set_src_lang_special_tokens(self._src_lang		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  str		)       ->  List[str]:
      return self.sp_model.encode(lowerCamelCase   ,						out_type=lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Dict:
      if token in self.lang_token_to_id:
        return self.lang_token_to_id[token]
      return self.encoder.get(lowerCamelCase   ,						self.encoder[self.unk_token]		)

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  int		)       ->  str:
      if index in self.id_to_lang_token:
        return self.id_to_lang_token[index]
      return self.decoder.get(lowerCamelCase   ,						self.unk_token		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  int		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[Any]      =       []
      lowerCAmelCase_      :				Optional[Any]      =       """"""
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += self.sp_model.decode(lowerCamelCase		) + token
          lowerCAmelCase_      :				str      =       []
        else:
          current_sub_tokens.append(lowerCamelCase		)
      out_string += self.sp_model.decode(lowerCamelCase		)
      return out_string.strip()

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None   ,						lowerCamelCase   :  bool = 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		)

      lowerCAmelCase_      :				Optional[Any]      =       [1] * len(self.prefix_tokens		)
      lowerCAmelCase_      :				Union[str, Any]      =       [1] * len(self.suffix_tokens		)
      if token_ids_a is None:
        return prefix_ones + ([0] * len(lowerCamelCase		)) + suffix_ones
      return prefix_ones + ([0] * len(lowerCamelCase		)) + ([0] * len(lowerCamelCase		)) + suffix_ones

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[int]   ,						lowerCamelCase   :  Optional[List[int]] = None		)       ->  List[int]:
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

    def __lowercase       (       self   :  List[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
      vocab.update(self.added_tokens_encoder		)
      return vocab

    def __getstate__(       self   :  Optional[int]		)       ->  Dict:
      lowerCAmelCase_      :				str      =       self.__dict__.copy()
      lowerCAmelCase_      :				Optional[Any]      =       None
      return state

    def __setstate__(       self   :  int   ,						lowerCamelCase   :  Dict		)       ->  None:
      lowerCAmelCase_      :				Tuple      =       d

      # for backward compatibility
      if not hasattr(self   ,						"""sp_model_kwargs"""		):
        lowerCAmelCase_      :				List[Any]      =       {}

      lowerCAmelCase_      :				Dict      =       load_spm(self.spm_file   ,						self.sp_model_kwargs		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[str] = None		)       ->  Tuple[str]:
      lowerCAmelCase_      :				List[Any]      =       Path(lowerCamelCase		)
      if not save_dir.is_dir():
        raise OSError(F'{save_directory} should be a directory'		)
      lowerCAmelCase_      :				str      =       save_dir / (
          (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
      )
      lowerCAmelCase_      :				Optional[int]      =       save_dir / (
          (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
      )

      save_json(self.encoder   ,						lowerCamelCase		)

      if os.path.abspath(self.spm_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.spm_file		):
        copyfile(self.spm_file   ,						lowerCamelCase		)
      elif not os.path.isfile(self.spm_file		):
        with open(lowerCamelCase   ,						"""wb"""		) as fi:
          lowerCAmelCase_      :				List[str]      =       self.sp_model.serialized_model_proto()
          fi.write(lowerCamelCase		)

      return (str(lowerCamelCase		), str(lowerCamelCase		))

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  str = "en"   ,						lowerCamelCase   :  Optional[List[str]] = None   ,						lowerCamelCase   :  str = "ro"   ,						**lowerCamelCase   :  List[Any]   ,						)       ->  BatchEncoding:
      lowerCAmelCase_      :				Tuple      =       src_lang
      lowerCAmelCase_      :				List[Any]      =       tgt_lang
      self.set_src_lang_special_tokens(self.src_lang		)
      return super().prepare_seqaseq_batch(lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Optional[str]   ,						lowerCamelCase   :  Optional[str]   ,						**lowerCamelCase   :  int		)       ->  Dict:
      if src_lang is None or tgt_lang is None:
        raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
      lowerCAmelCase_      :				int      =       src_lang
      lowerCAmelCase_      :				Optional[Any]      =       self(lowerCamelCase   ,						add_special_tokens=lowerCamelCase   ,						**lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       self.get_lang_id(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       tgt_lang_id
      return inputs

    def __lowercase       (       self   :  Tuple		)       ->  int:
      self.set_src_lang_special_tokens(self.src_lang		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  Union[str, Any]:
      self.set_tgt_lang_special_tokens(self.tgt_lang		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str		)       ->  None:
      lowerCAmelCase_      :				Tuple      =       self.get_lang_token(lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       self.lang_token_to_id[lang_token]
      lowerCAmelCase_      :				Optional[int]      =       [self.cur_lang_id]
      lowerCAmelCase_      :				Union[str, Any]      =       [self.eos_token_id]

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  str		)       ->  None:
      lowerCAmelCase_      :				Optional[Any]      =       self.get_lang_token(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       self.lang_token_to_id[lang_token]
      lowerCAmelCase_      :				Dict      =       [self.cur_lang_id]
      lowerCAmelCase_      :				int      =       [self.eos_token_id]

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  str		)       ->  str:
      return self.lang_code_to_token[lang]





    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str		)       ->  int:
      lowerCAmelCase_      :				List[Any]      =       self.get_lang_token(lowerCamelCase		)
      return self.lang_token_to_id[lang_token]







def     UpperCamelCase_      (   A__ :       str							,   A__ :       Dict[str, Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       sentencepiece.SentencePieceProcessor(**A__ )
  spm.Load(str(A__ ) )
  return spm







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  with open(A__							,   """r""" ) as f:
    return json.load(A__ )







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       str ):
  '''simple docstring'''





  with open(A__							,   """w""" ) as f:
    json.dump(A__							,   A__							,   indent=2 )







'''simple docstring'''






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        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple=13   ,						lowerCamelCase   :  Dict=30   ,						lowerCamelCase   :  Dict=2   ,						lowerCamelCase   :  Optional[int]=3   ,						lowerCamelCase   :  List[Any]=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  str=32   ,						lowerCamelCase   :  Any=5   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  List[str]=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  str=10   ,						lowerCamelCase   :  Optional[Any]=0.02   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Union[str, Any]=0.6   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[Any]      =       parent
      lowerCAmelCase_      :				Optional[int]      =       batch_size
      lowerCAmelCase_      :				int      =       image_size
      lowerCAmelCase_      :				List[Any]      =       patch_size
      lowerCAmelCase_      :				int      =       num_channels
      lowerCAmelCase_      :				Any      =       is_training
      lowerCAmelCase_      :				Tuple      =       use_labels
      lowerCAmelCase_      :				Optional[Any]      =       hidden_size
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Optional[Any]      =       num_attention_heads
      lowerCAmelCase_      :				Dict      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				List[Any]      =       type_sequence_label_size
      lowerCAmelCase_      :				Dict      =       initializer_range
      lowerCAmelCase_      :				List[str]      =       mask_ratio
      lowerCAmelCase_      :				Tuple      =       scope

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

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

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

      lowerCAmelCase_      :				str      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      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 __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  Tuple:
      lowerCAmelCase_      :				Tuple      =       ViTMAEModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       (self.image_size // self.patch_size) ** 2
      lowerCAmelCase_      :				int      =       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_      :				List[Any]      =       1
      lowerCAmelCase_      :				List[str]      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Optional[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_      :				Tuple      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       self.patch_size**2
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)





    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.prepare_config_and_inputs()
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       config_and_inputs
      lowerCAmelCase_      :				Any      =       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowercase       			= {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEModelTester(self		)
      lowerCAmelCase_      :				Optional[int]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Dict		)       ->  Tuple:
      self.config_tester.run_common_tests()

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

    def __lowercase       (       self   :  List[str]		)       ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Tuple      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

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

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

        lowerCAmelCase_      :				Optional[Any]      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Tuple      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]		)       ->  str:
      # make masks reproducible
      np.random.seed(2		)

      lowerCAmelCase_      :				Tuple      =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
      lowerCAmelCase_      :				Optional[Any]      =       torch.from_numpy(lowerCamelCase		)

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

      super().check_pt_tf_models(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

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

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2		)
        with torch.no_grad():
          lowerCAmelCase_      :				Any      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

        lowerCAmelCase_      :				Any      =       outputs[0].cpu().numpy()
        lowerCAmelCase_      :				List[str]      =       0

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

          # Make sure we don't have nans
          lowerCAmelCase_      :				Optional[Any]      =       after_outputs[0].cpu().numpy()
          lowerCAmelCase_      :				str      =       0
          lowerCAmelCase_      :				List[Any]      =       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 __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      pass

    @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   :  Union[str, Any]		)       ->  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 __lowercase       (       self   :  Optional[Any]		)       ->  Union[str, Any]:
      pass

    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      pass

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





    @slow
    def __lowercase       (       self   :  List[str]		)       ->  List[Any]:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				List[Any]      =       ViTMAEModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






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







    """simple docstring"""



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





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

      lowerCAmelCase_      :				Dict      =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       self.default_image_processor
      lowerCAmelCase_      :				Union[str, Any]      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       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)
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEConfig()
      lowerCAmelCase_      :				Optional[Any]      =       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_      :				str      =       model(**lowerCamelCase   ,						noise=torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)		)

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

      lowerCAmelCase_      :				str      =       torch.tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

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



