





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	UpperCamelCase_							(  SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_     ):
							lowerCamelCase_							     =       """swin"""

							lowerCamelCase_							     =       {
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}


							def __init__(				self     :List[str]				,					__A     :Any=224				,					__A     :int=4				,					__A     :str=3				,					__A     :Optional[Any]=96				,					__A     :Tuple=[2, 2, 6, 2]				,					__A     :Dict=[3, 6, 12, 24]				,					__A     :Dict=7				,					__A     :List[Any]=4.0				,					__A     :Union[str, Any]=True				,					__A     :List[str]=0.0				,					__A     :List[Any]=0.0				,					__A     :List[str]=0.1				,					__A     :Dict="gelu"				,					__A     :List[Any]=False				,					__A     :Union[str, Any]=0.0_2				,					__A     :Optional[int]=1E-5				,					__A     :Any=32				,					__A     :Tuple=None				,					__A     :Optional[Any]=None				,					**__A     :Union[str, Any]				,					) -> int:



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__								=   image_size
									SCREAMING_SNAKE_CASE__								=   patch_size
									SCREAMING_SNAKE_CASE__								=   num_channels
									SCREAMING_SNAKE_CASE__								=   embed_dim
									SCREAMING_SNAKE_CASE__								=   depths
									SCREAMING_SNAKE_CASE__								=   len(__A		)
									SCREAMING_SNAKE_CASE__								=   num_heads
									SCREAMING_SNAKE_CASE__								=   window_size
									SCREAMING_SNAKE_CASE__								=   mlp_ratio
									SCREAMING_SNAKE_CASE__								=   qkv_bias
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   drop_path_rate
									SCREAMING_SNAKE_CASE__								=   hidden_act
									SCREAMING_SNAKE_CASE__								=   use_absolute_embeddings
									SCREAMING_SNAKE_CASE__								=   layer_norm_eps
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   encoder_stride
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									SCREAMING_SNAKE_CASE__								=   int(embed_dim * 2 ** (len(__A		) - 1)		)
									SCREAMING_SNAKE_CASE__								=   ['stem'] + [f'''stage{idx}''' for idx in range(1				,					len(__A		) + 1		)]
									SCREAMING_SNAKE_CASE__								=   get_aligned_output_features_output_indices(
									    out_features=__A				,					out_indices=__A				,					stage_names=self.stage_names		)




class 	UpperCamelCase_							(  SCREAMING_SNAKE_CASE_     ):
							lowerCamelCase_							     =       version.parse("1.11"     )


							@property
							def   _snake_case	(				self     :Optional[int]		) -> Optional[int]:



									"""simple docstring"""



									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]		)


							@property
							def   _snake_case	(				self     :str		) -> int:



									"""simple docstring"""



									return 1E-4




"""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 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def __init__(	self				,   A				,   A=100				,   A=13				,   A=30				,   A=2				,   A=3				,   A=True				,   A=True				,   A=32				,   A=5				,   A=4				,   A=37				,   A="gelu"				,   A=0.1				,   A=0.1				,   A=10				,   A=0.0_2				,   A=3				,   ):
						_lowerCamelCase					:					str	       =    parent
						_lowerCamelCase					:					Optional[Any]	       =    vocab_size
						_lowerCamelCase					:					int	       =    batch_size
						_lowerCamelCase					:					Tuple	       =    image_size
						_lowerCamelCase					:					Optional[Any]	       =    patch_size
						_lowerCamelCase					:					Union[str, Any]	       =    num_channels
						_lowerCamelCase					:					Tuple	       =    is_training
						_lowerCamelCase					:					str	       =    use_labels
						_lowerCamelCase					:					Tuple	       =    hidden_size
						_lowerCamelCase					:					Tuple	       =    num_hidden_layers
						_lowerCamelCase					:					Any	       =    num_attention_heads
						_lowerCamelCase					:					int	       =    intermediate_size
						_lowerCamelCase					:					List[Any]	       =    hidden_act
						_lowerCamelCase					:					int	       =    hidden_dropout_prob
						_lowerCamelCase					:					Dict	       =    attention_probs_dropout_prob
						_lowerCamelCase					:					List[str]	       =    type_sequence_label_size
						_lowerCamelCase					:					int	       =    initializer_range

						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_lowerCamelCase					:					Optional[Any]	       =    (image_size // patch_size) ** 2
						_lowerCamelCase					:					Optional[int]	       =    num_patches + 1
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

						_lowerCamelCase					:					List[Any]	       =    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=A				,   initializer_range=self.initializer_range				,   )

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Tuple	       =    FlaxBeitModel(config=A							)
						_lowerCamelCase					:					List[str]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[str]	       =    FlaxBeitForMaskedImageModeling(config=A							)
						_lowerCamelCase					:					Any	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length - 1, self.vocab_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Optional[Any]	       =    self.type_sequence_label_size
						_lowerCamelCase					:					Tuple	       =    FlaxBeitForImageClassification(config=A							)
						_lowerCamelCase					:					Tuple	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						_lowerCamelCase					:					List[Any]	       =    1
						_lowerCamelCase					:					str	       =    FlaxBeitForImageClassification(A							)

						_lowerCamelCase					:					Optional[int]	       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						_lowerCamelCase					:					List[Any]	       =    model(A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					str	       =    self.prepare_config_and_inputs()
						(
						    (
						    _lowerCamelCase
						)       ,							(
						    _lowerCamelCase
						)       ,							(
						    _lowerCamelCase
						)       ,							
						)					:					Optional[Any]	       =    config_and_inputs
						_lowerCamelCase					:					int	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_flax
class 					A_(SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			List[Any]												=			(
			    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
			)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    FlaxBeitModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					List[str]	       =    model_class(A							)
									_lowerCamelCase					:					int	       =    inspect.signature(model.__call__							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_lowerCamelCase					:					Optional[Any]	       =    [*signature.parameters.keys()]

									_lowerCamelCase					:					List[Any]	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												_lowerCamelCase					:					Dict	       =    self._prepare_for_class(A				,   A							)
												_lowerCamelCase					:					str	       =    model_class(A							)

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

												with self.subTest('JIT Enabled'							):
															_lowerCamelCase					:					List[Any]	       =    model_jitted(**A							).to_tuple()

												with self.subTest('JIT Disabled'							):
															with jax.disable_jit():
																		_lowerCamelCase					:					Optional[int]	       =    model_jitted(**A							).to_tuple()

												self.assertEqual(len(A							)				,   len(A							)							)
												for jitted_output, output in zip(A				,   A							):
															self.assertEqual(jitted_output.shape				,   output.shape							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_class_name in self.all_model_classes:
									_lowerCamelCase					:					str	       =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'							)
									_lowerCamelCase					:					Dict	       =    model(np.ones((1, 3, 224, 224)							)							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'							) if is_vision_available() else None
			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					List[str]	       =    prepare_img()
						_lowerCamelCase					:					Any	       =    image_processor(images=A				,   return_tensors='np'							).pixel_values

						# prepare bool_masked_pos
						_lowerCamelCase					:					Optional[Any]	       =    np.ones((1, 196)				,   dtype=A							)

						# forward pass
						_lowerCamelCase					:					Optional[int]	       =    model(pixel_values=A				,   bool_masked_pos=A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					str	       =    (1, 196, 8192)
						self.assertEqual(logits.shape				,   A							)

						_lowerCamelCase					:					Dict	       =    np.array(
						    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]							)

						self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]				,   A				,   atol=1E-2							)							)
			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'							)

						_lowerCamelCase					:					Tuple	       =    self.default_image_processor
						_lowerCamelCase					:					Dict	       =    prepare_img()
						_lowerCamelCase					:					Optional[Any]	       =    image_processor(images=A				,   return_tensors='np'							)

						# forward pass
						_lowerCamelCase					:					Tuple	       =    model(**A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					int	       =    (1, 1000)
						self.assertEqual(logits.shape				,   A							)

						_lowerCamelCase					:					Tuple	       =    np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]							)

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

						_lowerCamelCase					:					str	       =    281
						self.assertEqual(logits.argmax(-1							).item()				,   A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'							)

						_lowerCamelCase					:					Any	       =    self.default_image_processor
						_lowerCamelCase					:					Union[str, Any]	       =    prepare_img()
						_lowerCamelCase					:					Tuple	       =    image_processor(images=A				,   return_tensors='np'							)

						# forward pass
						_lowerCamelCase					:					Union[str, Any]	       =    model(**A							)
						_lowerCamelCase					:					Optional[int]	       =    outputs.logits

						# verify the logits
						_lowerCamelCase					:					List[Any]	       =    (1, 2_1841)
						self.assertEqual(logits.shape				,   A							)

						_lowerCamelCase					:					Tuple	       =    np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]							)

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

						_lowerCamelCase					:					List[Any]	       =    2396
						self.assertEqual(logits.argmax(-1							).item()				,   A							)





