
'''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  ,  UpperCAmelCase  ,  UpperCAmelCase=100  ,  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  ,  )					->       Dict:
														_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 	lowercase							(self  )					->       Any:
														_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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														_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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
														_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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_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 	lowercase							(self  )					->       Tuple:
														_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   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

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

							def 	lowercase							(self  )					->       None:
														_snake_case       =  FlaxBeitModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  has_text_modality=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       Dict:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       str:
														_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 	lowercase							(self  )					->       Optional[Any]:
														_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 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       Optional[int]:
														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, 224, 224)  )  )
																					self.assertIsNotNone(UpperCAmelCase  )






def  __SCREAMING_SNAKE_CASE				(  ):
							_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 	lowercase							(self  )					->       Optional[int]:
														return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""  ) if is_vision_available() else None

							@slow
							def 	lowercase							(self  )					->       str:
														_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, 196)  ,  dtype=UpperCAmelCase  )

														# forward pass
														_snake_case       =  model(pixel_values=UpperCAmelCase  ,  bool_masked_pos=UpperCAmelCase  )
														_snake_case       =  outputs.logits

														# verify the logits
														_snake_case       =  (1, 196, 8192)
														self.assertEqual(logits.shape  ,  UpperCAmelCase  )

														_snake_case       =  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]  ,  UpperCAmelCase  ,  atol=1e-2  )  )

							@slow
							def 	lowercase							(self  )					->       str:
														_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, 1000)
														self.assertEqual(logits.shape  ,  UpperCAmelCase  )

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

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

														_snake_case       =  281
														self.assertEqual(logits.argmax(-1  ).item()  ,  UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       List[Any]:
														_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, 21841)
														self.assertEqual(logits.shape  ,  UpperCAmelCase  )

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

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

														_snake_case       =  2396
														self.assertEqual(logits.argmax(-1  ).item()  ,  UpperCAmelCase  )

'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .multicontrolnet import MultiControlNetModel
				from .pipeline_controlnet import StableDiffusionControlNetPipeline
				from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
				from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
				from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline