



import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import SwiftFormerForImageClassification, SwiftFormerModel
		from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor




class 							lowerCAmelCase__  :







					def __init__(       self      :     int			,		_A      :     Optional[int]			,		_A      :     Optional[Any]=13			,		_A      :     str=3			,		_A      :     List[Any]=True			,		_A      :     str=True			,		_A      :     Union[str, Any]=0.1			,		_A      :     str=0.1			,		_A      :     Tuple=224			,		_A      :     Union[str, Any]=1000			,		_A      :     int=[3, 3, 6, 4]			,		_A      :     Any=[48, 56, 112, 220]			,		):
											A__	:							Optional[Any]						    = parent
											A__	:							Optional[Any]						    = batch_size
											A__	:							Optional[Any]						    = num_channels
											A__	:							Union[str, Any]						    = is_training
											A__	:							str						    = use_labels
											A__	:							Any						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = num_labels
											A__	:							Any						    = image_size
											A__	:							Tuple						    = layer_depths
											A__	:							Optional[Any]						    = embed_dims







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

											A__	:							Tuple						    = None
											if self.use_labels:
																	A__	:							Tuple						    = ids_tensor([self.batch_size]			,		self.num_labels)

											A__	:							int						    = self.get_config()

											return config, pixel_values, labels







					def  _lowercase			(       self      :     int):
											return SwiftFormerConfig(
											    depths=self.layer_depths			,		embed_dims=self.embed_dims			,		mlp_ratio=4			,		downsamples=[True, True, True, True]			,		hidden_act="gelu"			,		num_labels=self.num_labels			,		down_patch_size=3			,		down_stride=2			,		down_pad=1			,		drop_rate=0.0			,		drop_path_rate=0.0			,		use_layer_scale=_A			,		layer_scale_init_value=1e-5			,		)







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     int			,		_A      :     List[Any]			,		_A      :     Optional[int]):
											A__	:							Union[str, Any]						    = SwiftFormerModel(config=_A)
											model.to(_A)
											model.eval()
											A__	:							Union[str, Any]						    = model(_A)
											self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.embed_dims[-1], 7, 7))







					def  _lowercase			(       self      :     Any			,		_A      :     int			,		_A      :     Any			,		_A      :     Any):
											A__	:							Tuple						    = self.num_labels
											A__	:							List[Any]						    = SwiftFormerForImageClassification(_A)
											model.to(_A)
											model.eval()
											A__	:							List[Any]						    = model(_A			,		labels=_A)
											self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))

											A__	:							int						    = SwiftFormerForImageClassification(_A)
											model.to(_A)
											model.eval()

											A__	:							Union[str, Any]						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
											A__	:							str						    = model(_A)
											self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))







					def  _lowercase			(       self      :     Tuple):
											((A__)    ,       (A__)    ,       (A__))	:							Optional[Any]						    = self.prepare_config_and_inputs()
											A__	:							Optional[int]						    = {"pixel_values": pixel_values}
											return config, inputs_dict




@require_torch
class 							lowerCAmelCase__  (	UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase						):
					__A     :    List[str]           =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					__A     :    List[str]           =						(
					    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__A     :    Dict           =						False
					__A     :    Optional[Any]           =						False
					__A     :    Optional[int]           =						False
					__A     :    Any           =						False
					__A     :    Optional[Any]           =						False







					def  _lowercase			(       self      :     int):
											A__	:							List[Any]						    = SwiftFormerModelTester(self)
											A__	:							List[str]						    = ConfigTester(
											    self			,		config_class=_A			,		has_text_modality=_A			,		hidden_size=37			,		num_attention_heads=12			,		num_hidden_layers=12			,		)







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







					@unittest.skip(reason="SwiftFormer does not use inputs_embeds")
					def  _lowercase			(       self      :     List[Any]):
											pass







					def  _lowercase			(       self      :     Optional[int]):
											A__    ,       A__	:							List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	A__	:							Union[str, Any]						    = model_class(_A)
																	A__	:							Optional[int]						    = model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_A			,		nn.Linear))







					def  _lowercase			(       self      :     int):
											A__    ,       A__	:							List[Any]						    = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	A__	:							Optional[Any]						    = model_class(_A)
																	A__	:							Tuple						    = inspect.signature(model.forward)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	A__	:							Optional[Any]						    = [*signature.parameters.keys()]

																	A__	:							Optional[int]						    = ["pixel_values"]
																	self.assertListEqual(arg_names[:1]			,		_A)







					def  _lowercase			(       self      :     Tuple):
											A__	:							Dict						    = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A)







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A)







					@slow
					def  _lowercase			(       self      :     Optional[Any]):
											for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__	:							List[str]						    = SwiftFormerModel.from_pretrained(_A)
																	self.assertIsNotNone(_A)







					@unittest.skip(reason="SwiftFormer does not output attentions")
					def  _lowercase			(       self      :     Optional[int]):
											pass







					def  _lowercase			(       self      :     str):
											def check_hidden_states_output(_A      :     Optional[int]			,		_A      :     List[Any]			,		_A      :     Optional[int]):
																	A__	:							List[Any]						    = model_class(_A)
																	model.to(_A)
																	model.eval()

																	with torch.no_grad():
																							A__	:							Any						    = model(**self._prepare_for_class(_A			,		_A))

																	A__	:							List[Any]						    = outputs.hidden_states

																	A__	:							Union[str, Any]						    = 8
																	self.assertEqual(len(_A)			,		_A)  # TODO

																	# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																	# with the width and height being successively divided by 2, after every 2 blocks
																	for i in range(len(_A)):
																							self.assertEqual(
																							    hidden_states[i].shape			,		torch.Size(
																							        [
																							            self.model_tester.batch_size,
																							            self.model_tester.embed_dims[i // 2],
																							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																							        ])			,		)

											A__    ,       A__	:							Tuple						    = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	A__	:							Union[str, Any]						    = True
																	check_hidden_states_output(_A			,		_A			,		_A)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	A__	:							List[str]						    = True

																	check_hidden_states_output(_A			,		_A			,		_A)







					def  _lowercase			(       self      :     Optional[Any]):
											def _config_zero_init(_A      :     Dict):
																	A__	:							List[Any]						    = copy.deepcopy(_A)
																	for key in configs_no_init.__dict__.keys():
																							if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																													setattr(_A			,		_A			,		1e-10)
																							if isinstance(getattr(_A			,		_A			,		_A)			,		_A):
																													A__	:							Optional[Any]						    = _config_zero_init(getattr(_A			,		_A))
																													setattr(_A			,		_A			,		_A)
																	return configs_no_init

											A__    ,       A__	:							Optional[Any]						    = self.model_tester.prepare_config_and_inputs_for_common()

											A__	:							Tuple						    = _config_zero_init(_A)
											for model_class in self.all_model_classes:
																	A__	:							Any						    = model_class(config=_A)
																	for name, param in model.named_parameters():
																							if param.requires_grad:
																													self.assertIn(
																													    ((param.data.mean() * 1e9) / 1e9).round().item()			,		[0.0, 1.0]			,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'			,		)







					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
					def  _lowercase			(       self      :     List[Any]):
											pass




def 	snake_case__       (					)		->		str:







						"""simple docstring"""

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




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







					@cached_property
					def  _lowercase			(       self      :     Optional[int]):
											return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs") if is_vision_available() else None







					@slow
					def  _lowercase			(       self      :     Dict):
											A__	:							int						    = SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs").to(_A)

											A__	:							List[str]						    = self.default_image_processor
											A__	:							Union[str, Any]						    = prepare_img()
											A__	:							Tuple						    = image_processor(images=_A			,		return_tensors="pt").to(_A)

											# forward pass
											with torch.no_grad():
																	A__	:							List[Any]						    = model(**_A)

											# verify the logits
											A__	:							List[str]						    = torch.Size((1, 1000))
											self.assertEqual(outputs.logits.shape			,		_A)

											A__	:							Union[str, Any]						    = torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]).to(_A)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		_A			,		atol=1e-4))




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])