

"""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 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		List[Any]    							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						lowerCAmelCase__      :		Union[str, Any]    							=      None
						if self.use_labels:
							lowerCAmelCase__      :		Any    							=      ids_tensor([self.batch_size]  ,self.num_labels						)

						lowerCAmelCase__      :		str    							=      self.get_config()

						return config, pixel_values, labels







					def 			__lowerCAmelCase					(			self     :							Optional[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=__UpperCamelCase  ,layer_scale_init_value=1E-5  ,)







					def 			__lowerCAmelCase					(			self     :							List[Any]  ,lowercase_     :							Any  ,lowercase_     :							int  ,lowercase_     :							Dict						):
						lowerCAmelCase__      :		Optional[int]    							=      SwiftFormerModel(config=__UpperCamelCase						)
						model.to(__UpperCamelCase						)
						model.eval()
						lowerCAmelCase__      :		List[str]    							=      model(__UpperCamelCase						)
						self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.embed_dims[-1], 7, 7)						)







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							Optional[int]						):
						lowerCAmelCase__      :		Dict    							=      self.num_labels
						lowerCAmelCase__      :		List[Any]    							=      SwiftFormerForImageClassification(__UpperCamelCase						)
						model.to(__UpperCamelCase						)
						model.eval()
						lowerCAmelCase__      :		int    							=      model(__UpperCamelCase  ,labels=__UpperCamelCase						)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)						)

						lowerCAmelCase__      :		List[Any]    							=      SwiftFormerForImageClassification(__UpperCamelCase						)
						model.to(__UpperCamelCase						)
						model.eval()

						lowerCAmelCase__      :		List[str]    							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
						lowerCAmelCase__      :		Dict    							=      model(__UpperCamelCase						)
						self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)						)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						((lowerCAmelCase__)							,(lowerCAmelCase__)							,(lowerCAmelCase__))      :		Optional[int]    							=      self.prepare_config_and_inputs()
						lowerCAmelCase__      :		Union[str, Any]    							=      {'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_torch
class 							SCREAMING_SNAKE_CASE   (						_a       ,				_a       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					lowercase__												=			(
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False
					lowercase__												=			False







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		List[str]    							=      SwiftFormerModelTester(self						)
						lowerCAmelCase__      :		Tuple    							=      ConfigTester(
						    self  ,config_class=__UpperCamelCase  ,has_text_modality=__UpperCamelCase  ,hidden_size=3_7  ,num_attention_heads=1_2  ,num_hidden_layers=1_2  ,)







					def 			__lowerCAmelCase					(			self     :							int						):
						self.config_tester.run_common_tests()







					@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''						)
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		List[str]    							=      model_class(__UpperCamelCase						)
							lowerCAmelCase__      :		Dict    							=      model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__UpperCamelCase  ,nn.Linear						)						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase__      :		List[Any]    							=      ['''pixel_values''']
							self.assertListEqual(arg_names[:1]  ,__UpperCamelCase						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Any    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)







					@slow
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__      :		List[Any]    							=      SwiftFormerModel.from_pretrained(__UpperCamelCase						)
							self.assertIsNotNone(__UpperCamelCase						)







					@unittest.skip(reason='''SwiftFormer does not output attentions'''						)
					def 			__lowerCAmelCase					(			self     :							Any						):
						pass







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						def check_hidden_states_output(lowercase_     :							Union[str, Any]  ,lowercase_     :							int  ,lowercase_     :							Optional[Any]						):
							lowerCAmelCase__      :		List[str]    							=      model_class(__UpperCamelCase						)
							model.to(__UpperCamelCase						)
							model.eval()

							with torch.no_grad():
								lowerCAmelCase__      :		Optional[int]    							=      model(**self._prepare_for_class(__UpperCamelCase  ,__UpperCamelCase						)						)

							lowerCAmelCase__      :		Tuple    							=      outputs.hidden_states

							lowerCAmelCase__      :		Tuple    							=      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),
								        ]						)  ,)

						lowerCAmelCase__							,lowerCAmelCase__      :		List[str]    							=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		str    							=      True
							check_hidden_states_output(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase						)

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

							check_hidden_states_output(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase						)







					def 			__lowerCAmelCase					(			self     :							Dict						):
						def _config_zero_init(lowercase_     :							Any						):
							lowerCAmelCase__      :		Union[str, Any]    							=      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						):
									lowerCAmelCase__      :		int    							=      _config_zero_init(getattr(__UpperCamelCase  ,__UpperCamelCase						)						)
									setattr(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase						)
							return configs_no_init

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

						lowerCAmelCase__      :		Dict    							=      _config_zero_init(__UpperCamelCase						)
						for model_class in self.all_model_classes:
							lowerCAmelCase__      :		Optional[int]    							=      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 			__lowerCAmelCase					(			self     :							List[str]						):
						pass

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		int    							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
	return image







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






					"""simple docstring"""



					@cached_property
					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''						) if is_vision_available() else None







					@slow
					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Dict    							=      SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''						).to(__UpperCamelCase						)

						lowerCAmelCase__      :		Any    							=      self.default_image_processor
						lowerCAmelCase__      :		List[Any]    							=      prepare_img()
						lowerCAmelCase__      :		Tuple    							=      image_processor(images=__UpperCamelCase  ,return_tensors='''pt'''						).to(__UpperCamelCase						)

						# forward pass
						with torch.no_grad():
							lowerCAmelCase__      :		Optional[int]    							=      model(**__UpperCamelCase						)

						# verify the logits
						lowerCAmelCase__      :		Union[str, Any]    							=      torch.Size((1, 1_0_0_0)						)
						self.assertEqual(outputs.logits.shape  ,__UpperCamelCase						)

						lowerCAmelCase__      :		Any    							=      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						)						)






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''					,			'''False'''    )    ) is not True					,			reason='''Skipping test because should only be run when releasing minor transformers version'''					,			)
@pytest.mark.usefixtures('''sm_env'''    )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]    )
class    a_ (			unittest.TestCase    ):






			def    UpperCamelCase_				(      self  ):
									if self.framework == "pytorch":
															subprocess.run(
															    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,			encoding="""utf-8"""				,			check=__UpperCamelCase				,			)
									assert hasattr(self				,			"""env"""  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# configuration for running training on smdistributed Model Parallel
									_lowercase					       =					{
									    """enabled""": True,
									    """processes_per_host""": 8,
									}
									_lowercase					       =					{
									    """enabled""": True,
									    """parameters""": {
									        """microbatches""": 4,
									        """placement_strategy""": """spread""",
									        """pipeline""": """interleaved""",
									        """optimize""": """speed""",
									        """partitions""": 4,
									        """ddp""": True,
									    },
									}

									_lowercase					       =					{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

									_lowercase					       =					"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,			source_dir=self.env.test_path				,			role=self.env.role				,			image_uri=self.env.image_uri				,			base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""				,			instance_count=__UpperCamelCase				,			instance_type=self.instance_type				,			debugger_hook_config=__UpperCamelCase				,			hyperparameters={
									        **self.env.hyperparameters,
									        """model_name_or_path""": self.model_name_or_path,
									        """max_steps""": 500,
									    }				,			metric_definitions=self.env.metric_definitions				,			distribution=__UpperCamelCase				,			py_version="""py36"""				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									TrainingJobAnalytics(__UpperCamelCase  ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""  )






			@parameterized.expand([(1,)]  )
			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# create estimator
									_lowercase					       =					self.create_estimator(__UpperCamelCase  )

									# run training
									estimator.fit()

									# result dataframe
									_lowercase					       =					TrainingJobAnalytics(estimator.latest_training_job.name  ).dataframe()

									# extract kpis
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
									_lowercase					       =					list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]  )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									_lowercase					       =					(
									    Session().describe_training_job(estimator.latest_training_job.name  ).get("""TrainingTimeInSeconds"""				,			999_999  )
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy  )
									assert all(t <= self.results["""eval_loss"""] for t in eval_loss  )

									# dump tests result into json file to share in PR
									with open(f"""{estimator.latest_training_job.name}.json"""				,			"""w"""  ) as outfile:
															json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}				,			__UpperCamelCase  )