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   lowercase      :




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict=13		,							_UpperCamelCase				:   Tuple=3		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   Union[str, Any]=0.1		,							_UpperCamelCase				:   Optional[Any]=0.1		,							_UpperCamelCase				:   List[Any]=224		,							_UpperCamelCase				:   List[Any]=1_000		,							_UpperCamelCase				:   str=[3, 3, 6, 4]		,							_UpperCamelCase				:   Tuple=[48, 56, 112, 220]		,							)   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    is_training
												SCREAMING_SNAKE_CASE           =    use_labels
												SCREAMING_SNAKE_CASE           =    hidden_dropout_prob
												SCREAMING_SNAKE_CASE           =    attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE           =    num_labels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    layer_depths
												SCREAMING_SNAKE_CASE           =    embed_dims




							def 		__snake_case(  self				:   List[Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

												SCREAMING_SNAKE_CASE           =    self.get_config()

												return config, pixel_values, labels




							def 		__snake_case(  self				:   int )   ->			Union[str, Any]:



												'''simple docstring'''

												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=_UpperCamelCase		,							layer_scale_init_value=1e-5		,							)




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Any )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    SwiftFormerModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(result.last_hidden_state.shape		,							(self.batch_size, self.embed_dims[-1], 7, 7) )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.num_labels
												SCREAMING_SNAKE_CASE           =    SwiftFormerForImageClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels) )

												SCREAMING_SNAKE_CASE           =    SwiftFormerForImageClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()

												SCREAMING_SNAKE_CASE           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels) )





							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												((SCREAMING_SNAKE_CASE)     ,				(SCREAMING_SNAKE_CASE)     ,				(SCREAMING_SNAKE_CASE))           =    self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE           =    {"pixel_values": pixel_values}
												return config, inputs_dict



@require_torch
class   lowercase      (       a			,    a			,    unittest.TestCase       ):
							lowercase__  :    List[Any]									=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							lowercase__  :    str									=							(
							    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowercase__  :    Union[str, Any]									=							False
							lowercase__  :    Tuple									=							False
							lowercase__  :    Optional[Any]									=							False
							lowercase__  :    List[Any]									=							False
							lowercase__  :    str									=							False




							def 		__snake_case(  self				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    SwiftFormerModelTester(self )
												SCREAMING_SNAKE_CASE           =    ConfigTester(
												    self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase		,							hidden_size=37		,							num_attention_heads=12		,							num_hidden_layers=12		,							)




							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="SwiftFormer does not use inputs_embeds" )
							def 		__snake_case(  self				:   Dict )   ->			Tuple:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   int )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_UpperCamelCase		,							nn.Linear ) )




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE           =    ["pixel_values"]
																	self.assertListEqual(arg_names[:1]		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Any )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )




							@slow
							def 		__snake_case(  self				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE           =    SwiftFormerModel.from_pretrained(_UpperCamelCase )
																	self.assertIsNotNone(_UpperCamelCase )




							@unittest.skip(reason="SwiftFormer does not output attentions" )
							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   Union[str, Any] )   ->			int:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   str ):
																	SCREAMING_SNAKE_CASE           =    model_class(_UpperCamelCase )
																	model.to(_UpperCamelCase )
																	model.eval()

																	with torch.no_grad():
																						SCREAMING_SNAKE_CASE           =    model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase ) )

																	SCREAMING_SNAKE_CASE           =    outputs.hidden_states

																	SCREAMING_SNAKE_CASE           =    8
																	self.assertEqual(len(_UpperCamelCase )		,							_UpperCamelCase )  # 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(_UpperCamelCase ) ):
																						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),
																						        ] )		,							)

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    True
																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	SCREAMING_SNAKE_CASE           =    True

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												def _config_zero_init(_UpperCamelCase				:   int ):
																	SCREAMING_SNAKE_CASE           =    copy.deepcopy(_UpperCamelCase )
																	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(_UpperCamelCase		,							_UpperCamelCase		,							1e-10 )
																						if isinstance(getattr(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )		,							_UpperCamelCase ):
																											SCREAMING_SNAKE_CASE           =    _config_zero_init(getattr(_UpperCamelCase		,							_UpperCamelCase ) )
																											setattr(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
																	return configs_no_init

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.model_tester.prepare_config_and_inputs_for_common()

												SCREAMING_SNAKE_CASE           =    _config_zero_init(_UpperCamelCase )
												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE           =    model_class(config=_UpperCamelCase )
																	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 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												pass





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
					return image


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




							@cached_property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			Tuple:



												'''simple docstring'''

												return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs" ) if is_vision_available() else None





							@slow
							def 		__snake_case(  self				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs" ).to(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.default_image_processor
												SCREAMING_SNAKE_CASE           =    prepare_img()
												SCREAMING_SNAKE_CASE           =    image_processor(images=_UpperCamelCase		,							return_tensors="pt" ).to(_UpperCamelCase )

												# forward pass
												with torch.no_grad():
																	SCREAMING_SNAKE_CASE           =    model(**_UpperCamelCase )

												# verify the logits
												SCREAMING_SNAKE_CASE           =    torch.Size((1, 1_000) )
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]] ).to(_UpperCamelCase )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





							def __repr__(  self				:   Union[str, Any] )   ->			str:



												'''simple docstring'''

												return pformat(self.adj_list )




