



import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=64	,	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=[1, 16, 4, 4]	,	A=None	,	)    ->    Any:
								UpperCAmelCase  :	Union[str, Any]							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Dict							  =	image_size
								UpperCAmelCase  :	List[Any]							  =	patch_size
								UpperCAmelCase  :	Optional[Any]							  =	num_channels
								UpperCAmelCase  :	int							  =	is_training
								UpperCAmelCase  :	Any							  =	use_labels
								UpperCAmelCase  :	Optional[Any]							  =	hidden_size
								UpperCAmelCase  :	Tuple							  =	num_hidden_layers
								UpperCAmelCase  :	List[str]							  =	num_attention_heads
								UpperCAmelCase  :	Dict							  =	intermediate_size
								UpperCAmelCase  :	Optional[Any]							  =	hidden_act
								UpperCAmelCase  :	Dict							  =	hidden_dropout_prob
								UpperCAmelCase  :	str							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Dict							  =	type_sequence_label_size
								UpperCAmelCase  :	Optional[Any]							  =	initializer_range
								UpperCAmelCase  :	Optional[int]							  =	scope
								UpperCAmelCase  :	List[Any]							  =	backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								UpperCAmelCase  :	Optional[Any]							  =	(self.image_size // 32) ** 2
								UpperCAmelCase  :	List[Any]							  =	num_patches + 1





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	List[str]							  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								UpperCAmelCase  :	Any							  =	None
								if self.use_labels:
															UpperCAmelCase  :	Tuple							  =	ids_tensor([self.batch_size]	,	self.type_sequence_label_size					)

								UpperCAmelCase  :	Dict							  =	self.get_config()

								return config, pixel_values, labels





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	List[str]							  =	{
								    """global_padding""": """same""",
								    """layer_type""": """bottleneck""",
								    """depths""": [3, 4, 9],
								    """out_features""": ["""stage1""", """stage2""", """stage3"""],
								    """embedding_dynamic_padding""": True,
								    """hidden_sizes""": [4, 8, 16, 32],
								    """num_groups""": 2,
								}

								return ViTHybridConfig(
								    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	,	backbone_featmap_shape=self.backbone_featmap_shape	,	backbone_config=A	,	)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	str							  =	ViTHybridModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[str]							  =	model(A					)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    int:
								UpperCAmelCase  :	str							  =	self.type_sequence_label_size
								UpperCAmelCase  :	int							  =	ViTHybridForImageClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Any							  =	model(A	,	labels=A					)
								self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size)					)






	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	List[Any]							  =	self.prepare_config_and_inputs()
								UpperCAmelCase  :	str							  =	config_and_inputs
								UpperCAmelCase  :	Union[str, Any]							  =	{"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class     UpperCamelCase_				(							__magic_name__   ,  __magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
	lowercase    =				(
	    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
	    if is_torch_available()
	    else {}
	)
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	List[str]							  =	ViTHybridModelTester(self					)
								UpperCAmelCase  :	Dict							  =	ConfigTester(self	,	config_class=A	,	has_text_modality=A	,	hidden_size=37					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								self.config_tester.run_common_tests()





	@unittest.skip(reason="""ViT does not use inputs_embeds"""					)
	def 				_lowercase(						self					)    ->    Any:
								pass





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	Any							  =	model_class(A					)
															self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module)					)
															UpperCAmelCase  :	Tuple							  =	model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(A	,	nn.Linear					)					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															UpperCAmelCase  :	Tuple							  =	model_class(A					)
															UpperCAmelCase  :	List[str]							  =	inspect.signature(model.forward					)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															UpperCAmelCase  :	str							  =	[*signature.parameters.keys()]

															UpperCAmelCase  :	List[Any]							  =	["""pixel_values"""]
															self.assertListEqual(arg_names[:1]	,	A					)





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	str							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase  :	int							  =	_config_zero_init(A					)
								for model_class in self.all_model_classes:
															UpperCAmelCase  :	int							  =	model_class(config=A					)
															# Skip the check for the backbone
															for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																													UpperCAmelCase  :	str							  =	[f'''{name}.{key}''' for key in module.state_dict().keys()]
																													break

															for name, param in model.named_parameters():
																						if param.requires_grad:
																													if name in backbone_params:
																																				continue
																													self.assertIn(
																													    ((param.data.mean() * 1e9).round() / 1e9).item()	,	[0.0, 1.0]	,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,	)






	@slow
	def 				_lowercase(						self					)    ->    str:
								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase  :	List[str]							  =	ViTHybridModel.from_pretrained(A					)
															self.assertIsNotNone(A					)




def    __lowerCamelCase						(      )   ->    Tuple:
							UpperCAmelCase  :	Any							  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
							return image


@require_torch
@require_vision
class     UpperCamelCase_				(							unittest.TestCase    ):





	@cached_property
	def 				_lowercase(						self					)    ->    Optional[int]:
								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)





	@slow
	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	int							  =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    A					)

								UpperCAmelCase  :	int							  =	self.default_image_processor
								UpperCAmelCase  :	List[Any]							  =	prepare_img()
								UpperCAmelCase  :	Tuple							  =	image_processor(images=A	,	return_tensors="""pt"""					).to(A					)

								# forward pass
								with torch.no_grad():
															UpperCAmelCase  :	str							  =	model(**A					)

								# verify the logits
								UpperCAmelCase  :	Tuple							  =	torch.Size((1, 1000)					)
								self.assertEqual(outputs.logits.shape	,	A					)

								UpperCAmelCase  :	int							  =	torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]					).to(A					)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	A	,	atol=1e-4					)					)






	@slow
	@require_accelerate
	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Any							  =	ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""					)
								UpperCAmelCase  :	Optional[int]							  =	ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""	,	device_map="""auto"""					)

								UpperCAmelCase  :	Dict							  =	prepare_img()

								UpperCAmelCase  :	List[str]							  =	image_processor(images=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	Any							  =	model(**A					)
								UpperCAmelCase  :	List[Any]							  =	outputs.logits
								# model predicts one of the 1000 ImageNet classes
								UpperCAmelCase  :	Tuple							  =	logits.argmax(-1					).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]	,	"""tabby, tabby cat"""					)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

