


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	,   _UpperCAmelCase	,   _UpperCAmelCase=1_00	,   _UpperCAmelCase=13	,   _UpperCAmelCase=30	,   _UpperCAmelCase=2	,   _UpperCAmelCase=3	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=10	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=3	,   ):
       snake_case_ =   parent
       snake_case_ =   vocab_size
       snake_case_ =   batch_size
       snake_case_ =   image_size
       snake_case_ =   patch_size
       snake_case_ =   num_channels
       snake_case_ =   is_training
       snake_case_ =   use_labels
       snake_case_ =   hidden_size
       snake_case_ =   num_hidden_layers
       snake_case_ =   num_attention_heads
       snake_case_ =   intermediate_size
       snake_case_ =   hidden_act
       snake_case_ =   hidden_dropout_prob
       snake_case_ =   attention_probs_dropout_prob
       snake_case_ =   type_sequence_label_size
       snake_case_ =   initializer_range

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       snake_case_ =   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=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   )

       return config, pixel_values, labels



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   FlaxBeitModel(config=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   FlaxBeitForMaskedImageModeling(config=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length - 1, self.vocab_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.type_sequence_label_size
       snake_case_ =   FlaxBeitForImageClassification(config=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   FlaxBeitForImageClassification(_UpperCAmelCase      )

       snake_case_ =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       snake_case_ =   model(_UpperCAmelCase      )





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







@require_flax
class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FlaxBeitModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   has_text_modality=_UpperCAmelCase	,   hidden_size=37      )



 def 		UpperCamelCase__  (			self      ):
       self.config_tester.run_common_tests()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_ =   ['''pixel_values''']
             self.assertListEqual(arg_names[:1]	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__      ):
                   snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase      )
                   snake_case_ =   model_class(_UpperCAmelCase      )

                   @jax.jit
                   def model_jitted(_UpperCAmelCase	,   **_UpperCAmelCase      ):
                         return model(pixel_values=_UpperCAmelCase	,   **_UpperCAmelCase      )

                   with self.subTest('''JIT Enabled'''      ):
                         snake_case_ =   model_jitted(**_UpperCAmelCase      ).to_tuple()

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

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       for model_class_name in self.all_model_classes:
             snake_case_ =   model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
             snake_case_ =   model(np.ones((1, 3, 2_24, 2_24)      )      )
             self.assertIsNotNone(_UpperCAmelCase      )






def  __lowerCAmelCase					()->						Tuple:




      """simple docstring"""

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







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

 '''simple docstring'''

 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''np'''      ).pixel_values

       # prepare bool_masked_pos
       snake_case_ =   np.ones((1, 1_96)	,   dtype=_UpperCAmelCase      )

       # forward pass
       snake_case_ =   model(pixel_values=_UpperCAmelCase	,   bool_masked_pos=_UpperCAmelCase      )
       snake_case_ =   outputs.logits

       # verify the logits
       snake_case_ =   (1, 1_96, 81_92)
       self.assertEqual(logits.shape	,   _UpperCAmelCase      )

       snake_case_ =   np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

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



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''np'''      )

       # forward pass
       snake_case_ =   model(**_UpperCAmelCase      )
       snake_case_ =   outputs.logits

       # verify the logits
       snake_case_ =   (1, 10_00)
       self.assertEqual(logits.shape	,   _UpperCAmelCase      )

       snake_case_ =   np.array([-1.2_385, -1.0_987, -1.0_108]      )

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

       snake_case_ =   2_81
       self.assertEqual(logits.argmax(-1      ).item()	,   _UpperCAmelCase      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''np'''      )

       # forward pass
       snake_case_ =   model(**_UpperCAmelCase      )
       snake_case_ =   outputs.logits

       # verify the logits
       snake_case_ =   (1, 2_18_41)
       self.assertEqual(logits.shape	,   _UpperCAmelCase      )

       snake_case_ =   np.array([1.6_881, -0.2_787, 0.5_901]      )

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

       snake_case_ =   23_96
       self.assertEqual(logits.argmax(-1      ).item()	,   _UpperCAmelCase      )



from collections import namedtuple

UpperCAmelCase    =					namedtuple("""from_to""", """from_ to""")

UpperCAmelCase    =					{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if from_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      if to_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()