




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       SCREAMING_SNAKE_CASE__					:


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


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			Optional[Any]					):
										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=lowerCAmelCase						,						layer_scale_init_value=1e-5						,						)


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


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				self.num_labels
										lowerCAmelCase  =				SwiftFormerForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)					)

										lowerCAmelCase  =				SwiftFormerForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

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




						def   __lowercase (							self				:			Any					):
										(lowerCAmelCase)  =				self.prepare_config_and_inputs()
										lowerCAmelCase  =				{"pixel_values": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_UpperCamelCase						,				_UpperCamelCase						,				unittest.TestCase					):
						_a    						=						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
						_a    						=						(
						    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				SwiftFormerModelTester(self					)
										lowerCAmelCase  =				ConfigTester(
										    self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37						,						num_attention_heads=12						,						num_hidden_layers=12						,						)


						def   __lowercase (							self				:			Any					):
										self.config_tester.run_common_tests()


						@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""					)
						def   __lowercase (							self				:			str					):
										pass


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

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


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)


						@slow
						def   __lowercase (							self				:			Any					):
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				SwiftFormerModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)


						@unittest.skip(reason="""SwiftFormer does not output attentions"""					)
						def   __lowercase (							self				:			Union[str, Any]					):
										pass


						def   __lowercase (							self				:			Tuple					):
										def check_hidden_states_output(lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			int					):
														lowerCAmelCase  =				model_class(lowerCAmelCase					)
														model.to(lowerCAmelCase					)
														model.eval()

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

														lowerCAmelCase  =				outputs.hidden_states

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

										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

														check_hidden_states_output(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]					):
										def _config_zero_init(lowerCAmelCase				:			Any					):
														lowerCAmelCase  =				copy.deepcopy(lowerCAmelCase					)
														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(lowerCAmelCase						,						lowerCAmelCase						,						1e-10					)
																		if isinstance(getattr(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)						,						lowerCAmelCase					):
																						lowerCAmelCase  =				_config_zero_init(getattr(lowerCAmelCase						,						lowerCAmelCase					)					)
																						setattr(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
														return configs_no_init

										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

										lowerCAmelCase  =				_config_zero_init(lowerCAmelCase					)
										for model_class in self.all_model_classes:
														lowerCAmelCase  =				model_class(config=lowerCAmelCase					)
														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				:			Optional[Any]					):
										pass





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






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






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


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




						@slow
						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""					).to(lowerCAmelCase					)

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

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

										# verify the logits
										lowerCAmelCase  =				torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape						,						lowerCAmelCase					)

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











"""simple docstring"""







import os
import string
import sys


a      =							1 << 8

a      =							{
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 2_7,
    'up': 6_5 + ARROW_KEY_FLAG,
    'down': 6_6 + ARROW_KEY_FLAG,
    'right': 6_7 + ARROW_KEY_FLAG,
    'left': 6_8 + ARROW_KEY_FLAG,
    'mod_int': 9_1,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 5_0,
    'delete': 5_1,
    'pg_up': 5_3,
    'pg_down': 5_4,
}

a      =							KEYMAP['up']
a      =							KEYMAP['left']

if sys.platform == "win32":
		a      =							[]
		a      =							{
		    b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
		    b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
		    b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
		    b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
		    b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
		}

for i in range(1_0):
		a      =							ord(str(i))





def 							lowercase					()     ->  str:


				'''simple docstring'''






				if os.name == "nt":
								import msvcrt

								lowerCAmelCase  =				"""mbcs"""
								# Flush the keyboard buffer
								while msvcrt.kbhit():
												msvcrt.getch()
								if len(snake_case__ ) == 0:
												# Read the keystroke
												lowerCAmelCase  =				msvcrt.getch()

												# If it is a prefix char, get second part
												if ch in (b"\x00", b"\xe0"):
																lowerCAmelCase  =				ch + msvcrt.getch()
																# Translate actual Win chars to bullet char types
																try:
																				lowerCAmelCase  =				chr(WIN_KEYMAP[cha] )
																				WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
																				WIN_CH_BUFFER.append(snake_case__ )
																				if ord(snake_case__ ) in (
																				    KEYMAP["insert"] - 1 << 9,
																				    KEYMAP["delete"] - 1 << 9,
																				    KEYMAP["pg_up"] - 1 << 9,
																				    KEYMAP["pg_down"] - 1 << 9,
																				):
																								WIN_CH_BUFFER.append(chr(126 ) )
																				lowerCAmelCase  =				chr(KEYMAP["""esc"""] )
																except KeyError:
																				lowerCAmelCase  =				cha[1]
												else:
																lowerCAmelCase  =				ch.decode(snake_case__ )
								else:
												lowerCAmelCase  =				WIN_CH_BUFFER.pop(0 )
				elif os.name == "posix":
								import termios
								import tty

								lowerCAmelCase  =				sys.stdin.fileno()
								lowerCAmelCase  =				termios.tcgetattr(snake_case__ )
								try:
												tty.setraw(snake_case__ )
												lowerCAmelCase  =				sys.stdin.read(1 )
								finally:
												termios.tcsetattr(snake_case__      ,       termios.TCSADRAIN      ,       snake_case__ )
				return ch






def 							lowercase					()     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				get_raw_chars()
				if ord(snake_case__ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
								return char

				elif ord(snake_case__ ) == KEYMAP["esc"]:
								lowerCAmelCase  =				get_raw_chars()
								if ord(snake_case__ ) == KEYMAP["mod_int"]:
												lowerCAmelCase  =				get_raw_chars()
												if ord(snake_case__ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
																return chr(ord(snake_case__ ) + ARROW_KEY_FLAG )
												else:
																return KEYMAP["undefined"]
								else:
												return get_raw_chars()

				else:
								if char in string.printable:
												return char
								else:
												return KEYMAP["undefined"]





