






"""simple docstring"""

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 						A_							:







				def __init__(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						str=3						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						Optional[Any]=0.1						,    __lowerCamelCase							:						List[str]=0.1						,    __lowerCamelCase							:						Optional[int]=2_2_4						,    __lowerCamelCase							:						Optional[Any]=1_0_0_0						,    __lowerCamelCase							:						Dict=[3, 3, 6, 4]						,    __lowerCamelCase							:						str=[4_8, 5_6, 1_1_2, 2_2_0]						,    )				->					List[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     num_channels
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     num_labels
										__magic_name__            =     image_size
										__magic_name__            =     layer_depths
										__magic_name__            =     embed_dims







				def     _snake_case					(							self							:						Union[str, Any]	)				->					str:
										__magic_name__            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

										__magic_name__            =     self.get_config()

										return config, pixel_values, labels







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







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     self.num_labels
										__magic_name__            =     SwiftFormerForImageClassification(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()
										__magic_name__            =     model(__lowerCamelCase						,    labels=__lowerCamelCase	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)	)

										__magic_name__            =     SwiftFormerForImageClassification(__lowerCamelCase	)
										model.to(__lowerCamelCase	)
										model.eval()

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







				def     _snake_case					(							self							:						Dict	)				->					Any:
										(__magic_name__)            =     self.prepare_config_and_inputs()
										__magic_name__            =     {"pixel_values": pixel_values}
										return config, inputs_dict





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

				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						List[Any]	)				->					int:
										__magic_name__            =     SwiftFormerModelTester(self	)
										__magic_name__            =     ConfigTester(
										    self						,    config_class=__lowerCamelCase						,    has_text_modality=__lowerCamelCase						,    hidden_size=3_7						,    num_attention_heads=1_2						,    num_hidden_layers=1_2						,    )







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







				@unittest.skip(reason="SwiftFormer does not use inputs_embeds"	)
				def     _snake_case					(							self							:						Optional[int]	)				->					int:
										pass







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

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







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

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







				def     _snake_case					(							self							:						Any	)				->					int:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase	)







				def     _snake_case					(							self							:						Dict	)				->					Any:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase	)







				@slow
				def     _snake_case					(							self							:						Any	)				->					int:
										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__            =     SwiftFormerModel.from_pretrained(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)







				@unittest.skip(reason="SwiftFormer does not output attentions"	)
				def     _snake_case					(							self							:						Dict	)				->					Any:
										pass







				def     _snake_case					(							self							:						str	)				->					int:
										def check_hidden_states_output(__lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Dict	):
																__magic_name__            =     model_class(__lowerCamelCase	)
																model.to(__lowerCamelCase	)
																model.eval()

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

																__magic_name__            =     outputs.hidden_states

																__magic_name__            =     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),
																						        ]	)						,    )

										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

																check_hidden_states_output(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[int]:
										def _config_zero_init(__lowerCamelCase							:						str	):
																__magic_name__            =     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	):
																												__magic_name__            =     _config_zero_init(getattr(__lowerCamelCase						,    __lowerCamelCase	)	)
																												setattr(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
																return configs_no_init

										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

										__magic_name__            =     _config_zero_init(__lowerCamelCase	)
										for model_class in self.all_model_classes:
																__magic_name__            =     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     _snake_case					(							self							:						List[Any]	)				->					int:
										pass






def _lowerCAmelCase	(  ):

						'''simple docstring'''






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


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







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







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										__magic_name__            =     SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"	).to(__lowerCamelCase	)

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

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

										# verify the logits
										__magic_name__            =     torch.Size((1, 1_0_0_0)	)
										self.assertEqual(outputs.logits.shape						,    __lowerCamelCase	)

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














"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



