
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				,			__UpperCamelCase				,			__UpperCamelCase=13				,			__UpperCamelCase=3				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.1				,			__UpperCamelCase=224				,			__UpperCamelCase=1_000				,			__UpperCamelCase=[3, 3, 6, 4]				,			__UpperCamelCase=[48, 56, 112, 220]				,			):
									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					is_training
									_lowercase					       =					use_labels
									_lowercase					       =					hidden_dropout_prob
									_lowercase					       =					attention_probs_dropout_prob
									_lowercase					       =					num_labels
									_lowercase					       =					image_size
									_lowercase					       =					layer_depths
									_lowercase					       =					embed_dims






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

									_lowercase					       =					self.get_config()

									return config, pixel_values, labels






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					SwiftFormerModel(config=__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					model(__UpperCamelCase  )
									self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.embed_dims[-1], 7, 7)  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					self.num_labels
									_lowercase					       =					SwiftFormerForImageClassification(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()
									_lowercase					       =					model(__UpperCamelCase				,			labels=__UpperCamelCase  )
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)  )

									_lowercase					       =					SwiftFormerForImageClassification(__UpperCamelCase  )
									model.to(__UpperCamelCase  )
									model.eval()

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






			def    UpperCamelCase_				(      self  ):
									((_lowercase)       ,						(_lowercase)       ,						(_lowercase))					       =					self.prepare_config_and_inputs()
									_lowercase					       =					{"""pixel_values""": pixel_values}
									return config, inputs_dict







@require_torch
class    a_ (			_a					,			_a					,			unittest.TestCase    ):
			a	:			Optional[Any]					   =							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			a	:			str					   =							(
			    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			a	:			List[Any]					   =							False
			a	:			Union[str, Any]					   =							False
			a	:			List[Any]					   =							False
			a	:			Any					   =							False
			a	:			Tuple					   =							False






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					SwiftFormerModelTester(self  )
									_lowercase					       =					ConfigTester(
									    self				,			config_class=__UpperCamelCase				,			has_text_modality=__UpperCamelCase				,			hidden_size=37				,			num_attention_heads=12				,			num_hidden_layers=12				,			)






			def    UpperCamelCase_				(      self  ):
									self.config_tester.run_common_tests()






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






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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






			def    UpperCamelCase_				(      self  ):
									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase  )






			@slow
			def    UpperCamelCase_				(      self  ):
									for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowercase					       =					SwiftFormerModel.from_pretrained(__UpperCamelCase  )
															self.assertIsNotNone(__UpperCamelCase  )






			@unittest.skip(reason="""SwiftFormer does not output attentions"""  )
			def    UpperCamelCase_				(      self  ):
									pass






			def    UpperCamelCase_				(      self  ):
									def check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					model_class(__UpperCamelCase  )
															model.to(__UpperCamelCase  )
															model.eval()

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

															_lowercase					       =					outputs.hidden_states

															_lowercase					       =					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),
																					        ]  )				,			)

									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

															check_hidden_states_output(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									def _config_zero_init(__UpperCamelCase  ):
															_lowercase					       =					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  ):
																											_lowercase					       =					_config_zero_init(getattr(__UpperCamelCase				,			__UpperCamelCase  )  )
																											setattr(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
															return configs_no_init

									_lowercase       ,						_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()

									_lowercase					       =					_config_zero_init(__UpperCamelCase  )
									for model_class in self.all_model_classes:
															_lowercase					       =					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    UpperCamelCase_				(      self  ):
									pass







def 	UpperCamelCase__  (     )  ->     Optional[int]:
						_lowercase					       =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
						return image



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






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""  ) if is_vision_available() else None






			@slow
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""  ).to(__UpperCamelCase  )

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

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

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

									_lowercase					       =					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  )