






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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







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


         """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  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """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  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Dict							)						->	List[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  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Dict							)						->	Optional[Any]:


         """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  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]							)						->	List[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  lowerCamelCase       (      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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )


  def  lowerCamelCase       (      self		:			str							)						->	None:


         """simple docstring"""
         _UpperCAmelCase							=    FlaxBeitModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Dict							)						->	str:


         """simple docstring"""
         self.config_tester.run_common_tests()


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


         """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  lowerCamelCase       (      self		:			List[str]							)						->	Union[str, 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		:			Union[str, Any]       ,				**lowerCamelCase		:			Dict							):
                              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  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Union[str, Any]:


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





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

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Union[str, Any]:


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


  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """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  lowerCamelCase       (      self		:			List[Any]							)						->	str:


         """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  lowerCamelCase       (      self		:			int							)						->	Optional[int]:


         """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, 2_1841)
         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 os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )