





'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def __init__(				self  :						Tuple			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						str=100			,					lowerCAmelCase__  :						str=13			,					lowerCAmelCase__  :						Optional[Any]=30			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Optional[int]=3			,					lowerCAmelCase__  :						List[Any]=True			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						Optional[Any]=32			,					lowerCAmelCase__  :						int=5			,					lowerCAmelCase__  :						str=4			,					lowerCAmelCase__  :						Tuple=37			,					lowerCAmelCase__  :						Optional[int]="gelu"			,					lowerCAmelCase__  :						Tuple=0.1			,					lowerCAmelCase__  :						List[str]=0.1			,					lowerCAmelCase__  :						Tuple=10			,					lowerCAmelCase__  :						Any=0.02			,					lowerCAmelCase__  :						List[str]=3			,					)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			patch_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			type_sequence_label_size
            _UpperCamelCase							       =			initializer_range

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





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

            _UpperCamelCase							       =			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			,					)

            return config, pixel_values, labels





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitModel(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)	)





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Any	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitForMaskedImageModeling(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.type_sequence_label_size
            _UpperCamelCase							       =			FlaxBeitForImageClassification(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.type_sequence_label_size)	)

            # test greyscale images
            _UpperCamelCase							       =			1
            _UpperCamelCase							       =			FlaxBeitForImageClassification(lowerCAmelCase__	)

            _UpperCamelCase							       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)







       def snake_case__		(				self  :						int	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self.prepare_config_and_inputs()
            (
                (
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							
            )							       =			config_and_inputs
            _UpperCamelCase							       =			{'''pixel_values''': pixel_values}
            return config, inputs_dict

@require_flax
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       (
           (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
       )





       def snake_case__		(				self  :						List[Any]	)       ->	None:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__			,					hidden_size=37	)





       def snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__		(				self  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                      _UpperCamelCase							       =			self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)
                      _UpperCamelCase							       =			model_class(lowerCAmelCase__	)

                      @jax.jit
                      def model_jitted(lowerCAmelCase__  :						Tuple			,					**lowerCAmelCase__  :						str	):
                           return model(pixel_values=lowerCAmelCase__			,					**lowerCAmelCase__	)

                      with self.subTest('''JIT Enabled'''	):
                           _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                _UpperCamelCase							       =			model_jitted(**lowerCAmelCase__	).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__	)			,					len(lowerCAmelCase__	)	)
                      for jitted_output, output in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                           self.assertEqual(jitted_output.shape			,					output.shape	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)







       @slow
       def snake_case__		(				self  :						int	)       ->	int:

            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 _UpperCamelCase							       =			model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''	)
                 _UpperCamelCase							       =			model(np.ones((1, 3, 224, 224)	)	)
                 self.assertIsNotNone(lowerCAmelCase__	)







def    a__							(							)						->						Optional[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_vision
@require_flax
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						List[str]	)       ->	Optional[int]:

            '''simple docstring'''




            return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''	) if is_vision_available() else None





       @slow
       def snake_case__		(				self  :						Optional[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	).pixel_values

            # prepare bool_masked_pos
            _UpperCamelCase							       =			np.ones((1, 196)			,					dtype=lowerCAmelCase__	)

            # forward pass
            _UpperCamelCase							       =			model(pixel_values=lowerCAmelCase__			,					bool_masked_pos=lowerCAmelCase__	)
            _UpperCamelCase							       =			outputs.logits

            # verify the logits
            _UpperCamelCase							       =			(1, 196, 8192)
            self.assertEqual(logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			np.array(
                [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]	)

            self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]			,					lowerCAmelCase__			,					atol=1e-2	)	)





       @slow
       def snake_case__		(				self  :						str	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	)

            # forward pass
            _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            _UpperCamelCase							       =			outputs.logits

            # verify the logits
            _UpperCamelCase							       =			(1, 1000)
            self.assertEqual(logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			np.array([-1.2385, -1.0987, -1.0108]	)

            self.assertTrue(np.allclose(logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)

            _UpperCamelCase							       =			281
            self.assertEqual(logits.argmax(-1	).item()			,					lowerCAmelCase__	)







       @slow
       def snake_case__		(				self  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''np'''	)

            # forward pass
            _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            _UpperCamelCase							       =			outputs.logits

            # verify the logits
            _UpperCamelCase							       =			(1, 21841)
            self.assertEqual(logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			np.array([1.6881, -0.2787, 0.5901]	)

            self.assertTrue(np.allclose(logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)

            _UpperCamelCase							       =			2396
            self.assertEqual(logits.argmax(-1	).item()			,					lowerCAmelCase__	)





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase  ={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

lowerCamelCase  =logging.WARNING


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					os.getenv('''DATASETS_VERBOSITY'''			,		UpperCamelCase__		)
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                      f'''has to be one of: { ", ".join(log_levels.keys()		) }'''		)
      return _default_log_level


def 				SCREAMING_SNAKE_CASE_   (      ):
      return __name__.split('''.'''		)[0]


def 				SCREAMING_SNAKE_CASE_   (      ):
      return logging.getLogger(_get_library_name()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      # Apply our default configuration to the library root logger.
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(_get_default_logging_level()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(logging.NOTSET		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = None		):
      if name is None:
            UpperCamelCase__     : Union[str, Any]											=					_get_library_name()
      return logging.getLogger(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return _get_library_root_logger().getEffectiveLevel()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      _get_library_root_logger().setLevel(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : str											=					False


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> int:  # pylint: disable=unused-argument




           """simple docstring"""







           UpperCamelCase__     : Dict											=					args[0] if args else None






     def __iter__(				self	)	-> Dict:




           """simple docstring"""







           return iter(self._iterator	)






     def __getattr__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           def empty_fn(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	):  # pylint: disable=unused-argument
                 return

           return empty_fn






     def __enter__(				self	)	-> Optional[int]:




           """simple docstring"""







           return self






     def __exit__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           return
lowerCamelCase  =True




class 					_lowerCamelCase			:





     """simple docstring"""







     def __call__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if _tqdm_active and not disable:
                 return tqdm_lib.tqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           else:
                 return EmptyTqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           if _tqdm_active:
                 return tqdm_lib.tqdm.set_lock(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           if _tqdm_active:
                 return tqdm_lib.tqdm.get_lock()
lowerCamelCase  =_tqdm_cls()


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      return bool(_tqdm_active		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Dict											=					True


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Union[str, Any]											=					False


