




'''simple docstring'''






import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   str:
      try:
            snake_case__       :   List[str]	      =    os.environ[key]
      except KeyError:
            # KEY isn't set, default to `default`.
            snake_case__       :   str	      =    default
      else:
            # KEY is set, convert it to True or False.
            try:
                  snake_case__       :   Optional[Any]	      =    strtobool(__SCREAMING_SNAKE_CASE		)
            except ValueError:
                  # More values are supported, but let's keep the message simple.
                  raise ValueError(f"If set, {key} must be yes or no."		)
      return _value


A_			      =		parse_flag_from_env("RUN_SLOW", default=False)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skip('Test was skipped'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(_run_slow_tests				,      'test is slow'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(not torch.cuda.is_available()				,      'test requires only a CPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      return unittest.skipUnless(torch.cuda.is_available()				,      'test requires a GPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      return unittest.skipUnless(is_xpu_available()				,      'test requires a XPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      return unittest.skipUnless(is_mps_available()				,      'test requires a `mps` backend support in `torch`'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(
          is_transformers_available() and is_datasets_available()				,      'test requires the Hugging Face suite'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Tuple:
      return unittest.skipUnless(is_bnb_available()				,      'test requires the bitsandbytes library'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[str]:
      return unittest.skipUnless(is_tpu_available()				,      'test requires TPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      return unittest.skipUnless(torch.cuda.device_count() == 1				,      'test requires a GPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      return unittest.skipUnless(torch.xpu.device_count() == 1				,      'test requires a XPU'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      return unittest.skipUnless(torch.cuda.device_count() > 1				,      'test requires multiple GPUs'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(torch.xpu.device_count() > 1				,      'test requires multiple XPUs'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      return unittest.skipUnless(is_safetensors_available()				,      'test requires safetensors'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Tuple:
      return unittest.skipUnless(is_deepspeed_available()				,      'test requires DeepSpeed'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Tuple:
      return unittest.skipUnless(is_torch_version('>='				,      '1.12.0'		)				,      'test requires torch version >= 1.12.0'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None		)    ->   Tuple:
      if test_case is None:
            return partial(__SCREAMING_SNAKE_CASE				,      version=__SCREAMING_SNAKE_CASE		)
      return unittest.skipUnless(is_torch_version('>='				,      __SCREAMING_SNAKE_CASE		)				,      f"test requires torch version >= {version}"		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Tuple:
      return unittest.skipUnless(is_tensorboard_available()				,      'test requires Tensorboard'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(is_wandb_available()				,      'test requires wandb'		)(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      return unittest.skipUnless(is_comet_ml_available()				,      'test requires comet_ml'		)(__SCREAMING_SNAKE_CASE		)


A_			      =		(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Any:
      return unittest.skipUnless(
          _atleast_one_tracker_available				,      'test requires at least one tracker to be available and for `comet_ml` to not be installed'				,      )(__SCREAMING_SNAKE_CASE		)






class        lowercase_      (						unittest.TestCase							):
     A_											=			True





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Optional[Any]			):
           snake_case__       :   Optional[Any]	      =    tempfile.mkdtemp()





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					Tuple			):
           if os.path.exists(cls.tmpdir			):
                 shutil.rmtree(cls.tmpdir			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           if self.clear_on_setup:
                 for path in Path(self.tmpdir			).glob('**/*'			):
                       if path.is_file():
                             path.unlink()
                       elif path.is_dir():
                             shutil.rmtree(__lowerCamelCase			)






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Any			):
           super().tearDown()
           # Reset the state of the AcceleratorState singleton.
           AcceleratorState._reset_state()
           PartialState._reset_state()






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Union[mock.Mock, List[mock.Mock]]			):
           snake_case__       :   Any	      =    mocks if isinstance(__lowerCamelCase     ,							(tuple, list)			) else [mocks]
           for m in self.mocks:
                 m.start()
                 self.addCleanup(m.stop			)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      snake_case__       :   List[Any]	      =    AcceleratorState()
      snake_case__       :   Any	      =    tensor[None].clone().to(state.device		)
      snake_case__       :   Any	      =    gather(__SCREAMING_SNAKE_CASE		).cpu()
      snake_case__       :   Optional[Any]	      =    tensor[0].cpu()
      for i in range(tensors.shape[0]		):
            if not torch.equal(tensors[i]				,      __SCREAMING_SNAKE_CASE		):
                  return False
      return True






class        lowercase_      :





     def __init__(     self     :					Optional[Any]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   List[Any]	      =    returncode
           snake_case__       :   Tuple	      =    stdout
           snake_case__       :   List[str]	      =    stderr






async def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      while True:
            snake_case__       :   str	      =    await stream.readline()
            if line:
                  callback(__SCREAMING_SNAKE_CASE		)
            else:
                  break






async def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=False				,      __SCREAMING_SNAKE_CASE=False		)    ->   _RunOutput:
      if echo:
            print('\nRunning: '				,      ' '.join(__SCREAMING_SNAKE_CASE		)		)

      snake_case__       :   Tuple	      =    await asyncio.create_subprocess_exec(
          cmd[0]				,      *cmd[1:]				,      stdin=__SCREAMING_SNAKE_CASE				,      stdout=asyncio.subprocess.PIPE				,      stderr=asyncio.subprocess.PIPE				,      env=__SCREAMING_SNAKE_CASE				,      )

      # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
      # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
      #
      # If it starts hanging, will need to switch to the following code. The problem is that no data
      # will be seen until it's done and if it hangs for example there will be no debug info.
      # out, err = await p.communicate()
      # return _RunOutput(p.returncode, out, err)

      snake_case__       :   List[Any]	      =    []
      snake_case__       :   Union[str, Any]	      =    []

      def tee(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=""		):
            snake_case__       :   str	      =    line.decode('utf-8'		).rstrip()
            sink.append(__SCREAMING_SNAKE_CASE		)
            if not quiet:
                  print(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      file=__SCREAMING_SNAKE_CASE		)

    # XXX: the timeout doesn't seem to make any difference here
      await asyncio.wait(
          [
              asyncio.create_task(_read_stream(p.stdout				,      lambda __SCREAMING_SNAKE_CASE		: tee(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      sys.stdout				,      label='stdout:'		)		)		),
              asyncio.create_task(_read_stream(p.stderr				,      lambda __SCREAMING_SNAKE_CASE		: tee(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      sys.stderr				,      label='stderr:'		)		)		),
          ]				,      timeout=__SCREAMING_SNAKE_CASE				,      )
      return _RunOutput(await p.wait()				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=None				,      __SCREAMING_SNAKE_CASE=180				,      __SCREAMING_SNAKE_CASE=False				,      __SCREAMING_SNAKE_CASE=True		)    ->   _RunOutput:
      snake_case__       :   str	      =    asyncio.get_event_loop()
      snake_case__       :   Tuple	      =    loop.run_until_complete(
          _stream_subprocess(__SCREAMING_SNAKE_CASE				,      env=__SCREAMING_SNAKE_CASE				,      stdin=__SCREAMING_SNAKE_CASE				,      timeout=__SCREAMING_SNAKE_CASE				,      quiet=__SCREAMING_SNAKE_CASE				,      echo=__SCREAMING_SNAKE_CASE		)		)

      snake_case__       :   List[Any]	      =    ' '.join(__SCREAMING_SNAKE_CASE		)
      if result.returncode > 0:
            snake_case__       :   Any	      =    '\n'.join(result.stderr		)
            raise RuntimeError(
                f"'{cmd_str}' failed with returncode {result.returncode}\n\n"
                f"The combined stderr from workers follows:\n{stderr}"		)

      return result






class        lowercase_      (						lowerCAmelCase_							):
     pass






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   Optional[Any]:
      try:
            snake_case__       :   Dict	      =    subprocess.check_output(__SCREAMING_SNAKE_CASE				,      stderr=subprocess.STDOUT		)
            if return_stdout:
                  if hasattr(__SCREAMING_SNAKE_CASE				,      'decode'		):
                        snake_case__       :   List[Any]	      =    output.decode('utf-8'		)
                  return output
      except subprocess.CalledProcessError as e:
            raise SubprocessCallException(
                f"Command `{' '.join(__SCREAMING_SNAKE_CASE		)}` failed with the following error:\n\n{e.output.decode()}"		) from e






'''simple docstring'''






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor






class        lowercase_      :





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Any=100     ,							__lowerCamelCase     :					str=13     ,							__lowerCamelCase     :					str=30     ,							__lowerCamelCase     :					int=2     ,							__lowerCamelCase     :					List[Any]=3     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					Optional[int]=True     ,							__lowerCamelCase     :					List[Any]=32     ,							__lowerCamelCase     :					Union[str, Any]=4     ,							__lowerCamelCase     :					str=4     ,							__lowerCamelCase     :					Tuple=37     ,							__lowerCamelCase     :					List[Any]="gelu"     ,							__lowerCamelCase     :					Optional[Any]=0.1     ,							__lowerCamelCase     :					List[str]=0.1     ,							__lowerCamelCase     :					Union[str, Any]=10     ,							__lowerCamelCase     :					int=0.0_2     ,							__lowerCamelCase     :					Tuple=3     ,							__lowerCamelCase     :					int=None     ,							__lowerCamelCase     :					Tuple=[0, 1, 2, 3]     ,							):
           snake_case__       :   Optional[int]	      =    parent
           snake_case__       :   Union[str, Any]	      =    100
           snake_case__       :   Union[str, Any]	      =    batch_size
           snake_case__       :   Dict	      =    image_size
           snake_case__       :   Tuple	      =    patch_size
           snake_case__       :   Dict	      =    num_channels
           snake_case__       :   List[Any]	      =    is_training
           snake_case__       :   Optional[Any]	      =    use_labels
           snake_case__       :   Optional[int]	      =    hidden_size
           snake_case__       :   Optional[int]	      =    num_hidden_layers
           snake_case__       :   Optional[Any]	      =    num_attention_heads
           snake_case__       :   Union[str, Any]	      =    intermediate_size
           snake_case__       :   str	      =    hidden_act
           snake_case__       :   Tuple	      =    hidden_dropout_prob
           snake_case__       :   Dict	      =    attention_probs_dropout_prob
           snake_case__       :   List[Any]	      =    type_sequence_label_size
           snake_case__       :   Dict	      =    initializer_range
           snake_case__       :   str	      =    scope
           snake_case__       :   Optional[Any]	      =    out_indices
           snake_case__       :   List[Any]	      =    num_labels

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           snake_case__       :   Optional[Any]	      =    (image_size // patch_size) ** 2
           snake_case__       :   Dict	      =    num_patches + 1





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Tuple	      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           snake_case__       :   Tuple	      =    None
           snake_case__       :   str	      =    None
           if self.use_labels:
                 snake_case__       :   Union[str, Any]	      =    ids_tensor([self.batch_size]     ,							self.type_sequence_label_size			)
                 snake_case__       :   Optional[int]	      =    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,							self.num_labels			)

           snake_case__       :   str	      =    self.get_config()

           return config, pixel_values, labels, pixel_labels





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return BeitConfig(
               vocab_size=self.vocab_size     ,							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     ,							out_indices=self.out_indices     ,							)





     def 			_lowerCAmelCase      (     self     :					int     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int			):
           snake_case__       :   Tuple	      =    BeitModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   int	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)			)





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[int]			):
           snake_case__       :   Optional[int]	      =    BeitForMaskedImageModeling(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Dict	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length - 1, self.vocab_size)			)





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   List[Any]	      =    self.type_sequence_label_size
           snake_case__       :   int	      =    BeitForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)			)

           # test greyscale images
           snake_case__       :   Union[str, Any]	      =    1
           snake_case__       :   Any	      =    BeitForImageClassification(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()

           snake_case__       :   Union[str, Any]	      =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           snake_case__       :   str	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)			)





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					List[Any]			):
           snake_case__       :   str	      =    self.num_labels
           snake_case__       :   int	      =    BeitForSemanticSegmentation(__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   List[Any]	      =    model(__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
           snake_case__       :   Dict	      =    model(__lowerCamelCase     ,							labels=__lowerCamelCase			)
           self.parent.assertEqual(
               result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[str]	      =    self.prepare_config_and_inputs()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   int	      =    config_and_inputs
           snake_case__       :   Any	      =    {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": BeitModel,
             "image-classification": BeitForImageClassification,
             "image-segmentation": BeitForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_											=			False
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Any	      =    BeitModelTester(self			)
           snake_case__       :   List[str]	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							has_text_modality=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           self.config_tester.run_common_tests()





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





     @require_torch_multi_gpu
     @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'			)
     def 			_lowerCAmelCase      (     self     :					Any			):
           pass





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__      ,							snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs_for_common()

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





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case__       :   List[str]	      =    ['pixel_values']
                 self.assertListEqual(arg_names[:1]     ,							__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[str]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           if not self.model_tester.is_training:
                 return

           snake_case__      ,							snake_case__       :   Any	      =    self.model_tester.prepare_config_and_inputs_for_common()
           snake_case__       :   str	      =    True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if model_class in [*get_values(__lowerCamelCase			), BeitForMaskedImageModeling]:
                       continue

                 snake_case__       :   int	      =    model_class(__lowerCamelCase			)
                 model.to(__lowerCamelCase			)
                 model.train()
                 snake_case__       :   Dict	      =    self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase     ,							return_labels=__lowerCamelCase			)
                 snake_case__       :   Optional[Any]	      =    model(**__lowerCamelCase			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__      ,							snake_case__       :   Tuple	      =    self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
                 return

           snake_case__       :   List[str]	      =    False
           snake_case__       :   List[Any]	      =    True

           for model_class in self.all_model_classes:
                 # we don't test BeitForMaskedImageModeling
                 if (
                     model_class in [*get_values(__lowerCamelCase			), BeitForMaskedImageModeling]
                     or not model_class.supports_gradient_checkpointing
                 ):
                       continue

                 snake_case__       :   Any	      =    model_class(__lowerCamelCase			)
                 model.gradient_checkpointing_enable()
                 model.to(__lowerCamelCase			)
                 model.train()
                 snake_case__       :   Optional[int]	      =    self._prepare_for_class(__lowerCamelCase     ,							__lowerCamelCase     ,							return_labels=__lowerCamelCase			)
                 snake_case__       :   int	      =    model(**__lowerCamelCase			).loss
                 loss.backward()





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.model_tester.prepare_config_and_inputs_for_common()

           snake_case__       :   Tuple	      =    _config_zero_init(__lowerCamelCase			)
           for model_class in self.all_model_classes:
                 snake_case__       :   List[str]	      =    model_class(config=__lowerCamelCase			)
                 for name, param in model.named_parameters():
                       # we skip lambda parameters as these require special initial values
                       # determined by config.layer_scale_init_value
                       if "lambda" in name:
                             continue
                       if param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()     ,							[0.0, 1.0]     ,							msg=F"Parameter {name} of model {model_class} seems not properly initialized"     ,							)





     @slow
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   str	      =    BeitModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






def 		UpperCamelCase__      (		)    ->   int:
      snake_case__       :   Any	      =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
      return image






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





     @cached_property
     def 			_lowerCAmelCase      (     self     :					List[str]			):
           return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'			) if is_vision_available() else None





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Tuple	      =    BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'			).to(__lowerCamelCase			)

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

           # prepare bool_masked_pos
           snake_case__       :   Optional[Any]	      =    torch.ones((1, 196)     ,							dtype=torch.bool			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(pixel_values=__lowerCamelCase     ,							bool_masked_pos=__lowerCamelCase			)
           snake_case__       :   str	      =    outputs.logits

           # verify the logits
           snake_case__       :   Dict	      =    torch.Size((1, 196, 8192)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   int	      =    torch.tensor(
               [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]			).to(__lowerCamelCase			)

           self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,							__lowerCamelCase     ,							atol=1E-2			)			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           snake_case__       :   Optional[int]	      =    BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'			).to(__lowerCamelCase			)

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

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Dict	      =    outputs.logits

           # verify the logits
           snake_case__       :   List[Any]	      =    torch.Size((1, 1000)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Union[str, Any]	      =    torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			).to(__lowerCamelCase			)

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

           snake_case__       :   Any	      =    281
           self.assertEqual(logits.argmax(-1			).item()     ,							__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Any	      =    BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'			).to(
               __lowerCamelCase			)

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

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Union[str, Any]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Dict	      =    outputs.logits

           # verify the logits
           snake_case__       :   Optional[int]	      =    torch.Size((1, 21841)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Dict	      =    torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			).to(__lowerCamelCase			)

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

           snake_case__       :   Optional[Any]	      =    2396
           self.assertEqual(logits.argmax(-1			).item()     ,							__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Optional[Any]	      =    BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'			)
           snake_case__       :   str	      =    model.to(__lowerCamelCase			)

           snake_case__       :   List[str]	      =    BeitImageProcessor(do_resize=__lowerCamelCase     ,							size=640     ,							do_center_crop=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    load_dataset('hf-internal-testing/fixtures_ade20k'     ,							split='test'			)
           snake_case__       :   Union[str, Any]	      =    Image.open(ds[0]['file']			)
           snake_case__       :   Tuple	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   List[str]	      =    model(**__lowerCamelCase			)
           snake_case__       :   Any	      =    outputs.logits

           # verify the logits
           snake_case__       :   Optional[int]	      =    torch.Size((1, 150, 160, 160)			)
           self.assertEqual(logits.shape     ,							__lowerCamelCase			)

           snake_case__       :   Tuple	      =    version.parse(PIL.__version__			) < version.parse('9.0.0'			)

           if is_pillow_less_than_a:
                 snake_case__       :   Optional[Any]	      =    torch.tensor(
                     [
                         [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                         [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                         [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                     ]     ,							device=__lowerCamelCase     ,							)
           else:
                 snake_case__       :   Union[str, Any]	      =    torch.tensor(
                     [
                         [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                         [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                         [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
                     ]     ,							device=__lowerCamelCase     ,							)

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





     @slow
     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Tuple	      =    BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'			)
           snake_case__       :   int	      =    model.to(__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    BeitImageProcessor(do_resize=__lowerCamelCase     ,							size=640     ,							do_center_crop=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    load_dataset('hf-internal-testing/fixtures_ade20k'     ,							split='test'			)
           snake_case__       :   Optional[int]	      =    Image.open(ds[0]['file']			)
           snake_case__       :   Optional[int]	      =    image_processor(images=__lowerCamelCase     ,							return_tensors='pt'			).to(__lowerCamelCase			)

           # forward pass
           with torch.no_grad():
                 snake_case__       :   Optional[int]	      =    model(**__lowerCamelCase			)

           snake_case__       :   Tuple	      =    outputs.logits.detach().cpu()

           snake_case__       :   Any	      =    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase     ,							target_sizes=[(500, 300)]			)
           snake_case__       :   List[Any]	      =    torch.Size((500, 300)			)
           self.assertEqual(segmentation[0].shape     ,							__lowerCamelCase			)

           snake_case__       :   Tuple	      =    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase			)
           snake_case__       :   Optional[Any]	      =    torch.Size((160, 160)			)
           self.assertEqual(segmentation[0].shape     ,							__lowerCamelCase			)
