

'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class 					lowercase						(  unittest.TestCase ):


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=30    ,			_snake_case=2    ,			_snake_case=3    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=10    ,			_snake_case=0.02    ,			)							->				Any:
								UpperCAmelCase_		:      List[Any]       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      str       	=					patch_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_channels
								UpperCAmelCase_		:      Optional[int]       	=					is_training
								UpperCAmelCase_		:      Optional[Any]       	=					use_labels
								UpperCAmelCase_		:      str       	=					hidden_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      List[Any]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Any       	=					hidden_act
								UpperCAmelCase_		:      List[Any]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Tuple       	=					type_sequence_label_size
								UpperCAmelCase_		:      str       	=					initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_		:      List[str]       	=					(image_size // patch_size) ** 2
								UpperCAmelCase_		:      int       	=					num_patches + 1


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:      Dict       	=					ViTConfig(
								    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=_snake_case    ,			initializer_range=self.initializer_range    ,			)

								return config, pixel_values


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					FlaxViTModel(config=_snake_case)
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_		:      Any       	=					(self.image_size, self.image_size)
								UpperCAmelCase_		:      Tuple       	=					(self.patch_size, self.patch_size)
								UpperCAmelCase_		:      int       	=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, num_patches + 1, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.type_sequence_label_size
								UpperCAmelCase_		:      int       	=					FlaxViTForImageClassification(config=_snake_case)
								UpperCAmelCase_		:      str       	=					model(_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size))

								# test greyscale images
								UpperCAmelCase_		:      Tuple       	=					1
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxViTForImageClassification(_snake_case)

								UpperCAmelCase_		:      int       	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								UpperCAmelCase_		:      Optional[Any]       	=					model(_snake_case)





							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    
								)		:      List[str]       	=					config_and_inputs
								UpperCAmelCase_		:      Optional[Any]       	=					{'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      str=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


							def 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxViTModelTester(self)
								UpperCAmelCase_		:      Any       	=					ConfigTester(self    ,			config_class=_snake_case    ,			has_text_modality=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				str:
								self.config_tester.run_common_tests()


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


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      int       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_		:      Dict       	=					['pixel_values']
									self.assertListEqual(arg_names[:1]    ,			_snake_case)


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      List[Any]       	=					self._prepare_for_class(_snake_case    ,			_snake_case)
										UpperCAmelCase_		:      List[Any]       	=					model_class(_snake_case)

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

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Dict       	=					model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      Optional[Any]       	=					model_jitted(**_snake_case).to_tuple()

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





							@slow
							def 						_snake_case							(			self)							->				int:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:      Tuple       	=					model_class_name.from_pretrained('google/vit-base-patch16-224')
									UpperCAmelCase_		:      Optional[Any]       	=					model(np.ones((1, 3, 224, 224)))
									self.assertIsNotNone(_snake_case)









'''simple docstring'''


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 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					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
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






