

"""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							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			int=1_0_0,					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			List[Any]=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Any=3,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=3_2,					UpperCAmelCase__							:			Any=5,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Any=3_7,					UpperCAmelCase__							:			Optional[int]="gelu",					UpperCAmelCase__							:			Dict=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Dict=1_0,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			List[Any]=3,					):
    __lowercase								=						parent
    __lowercase								=						vocab_size
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

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


 def 					_lowercase (			self							:			int	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

    __lowercase								=						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 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						FlaxBeitModel(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxBeitForImageClassification(config=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxBeitForImageClassification(UpperCAmelCase__	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						FlaxBeitModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					has_text_modality=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1],					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
          __lowercase								=						model_class(UpperCAmelCase__	)

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

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**UpperCAmelCase__	).to_tuple()

          with self.subTest("JIT Disabled"	):
             with jax.disable_jit():
                __lowercase								=						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 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Union[str, Any]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(UpperCAmelCase__	)







def   _A  (  )	->						str:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




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







 """simple docstring"""







 @cached_property
 def 					_lowercase (			self							:			Optional[int]	):
    return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	).pixel_values

    # prepare bool_masked_pos
    __lowercase								=						np.ones((1, 1_9_6),					dtype=UpperCAmelCase__	)

    # forward pass
    __lowercase								=						model(pixel_values=UpperCAmelCase__,					bool_masked_pos=UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_9_6, 8_1_9_2)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

    __lowercase								=						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 					_lowercase (			self							:			Any	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 1_0_0_0)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

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

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

    __lowercase								=						2_8_1
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	)

    __lowercase								=						self.default_image_processor
    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=UpperCAmelCase__,					return_tensors="np"	)

    # forward pass
    __lowercase								=						model(**UpperCAmelCase__	)
    __lowercase								=						outputs.logits

    # verify the logits
    __lowercase								=						(1, 2_1_8_4_1)
    self.assertEqual(logits.shape,					UpperCAmelCase__	)

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

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

    __lowercase								=						2_3_9_6
    self.assertEqual(logits.argmax(-1	).item(),					UpperCAmelCase__	)








'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A					:  Dict											=      {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A					:  str											=      ["TimmBackbone"]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  __A					:  Dict											=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



