


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					"swin"

				_lowerCAmelCase      :      int      							=					{
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}



				def __init__(						self       ,		UpperCAmelCase__=224       ,		UpperCAmelCase__=4       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=96       ,		UpperCAmelCase__=[2, 2, 6, 2]       ,		UpperCAmelCase__=[3, 6, 12, 24]       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=4.0       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=0.0       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__="gelu"       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=0.02       ,		UpperCAmelCase__=1e-5       ,		UpperCAmelCase__=32       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		**UpperCAmelCase__       ,		):
								super().__init__(**UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				image_size
								SCREAMING_SNAKE_CASE__     							=				patch_size
								SCREAMING_SNAKE_CASE__     							=				num_channels
								SCREAMING_SNAKE_CASE__     							=				embed_dim
								SCREAMING_SNAKE_CASE__     							=				depths
								SCREAMING_SNAKE_CASE__     							=				len(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				num_heads
								SCREAMING_SNAKE_CASE__     							=				window_size
								SCREAMING_SNAKE_CASE__     							=				mlp_ratio
								SCREAMING_SNAKE_CASE__     							=				qkv_bias
								SCREAMING_SNAKE_CASE__     							=				hidden_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE__     							=				drop_path_rate
								SCREAMING_SNAKE_CASE__     							=				hidden_act
								SCREAMING_SNAKE_CASE__     							=				use_absolute_embeddings
								SCREAMING_SNAKE_CASE__     							=				layer_norm_eps
								SCREAMING_SNAKE_CASE__     							=				initializer_range
								SCREAMING_SNAKE_CASE__     							=				encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								SCREAMING_SNAKE_CASE__     							=				int(embed_dim * 2 ** (len(UpperCAmelCase__     ) - 1)     )
								SCREAMING_SNAKE_CASE__     							=				["stem"] + [f'''stage{idx}''' for idx in range(1       ,		len(UpperCAmelCase__     ) + 1     )]
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				get_aligned_output_features_output_indices(
								    out_features=UpperCAmelCase__       ,		out_indices=UpperCAmelCase__       ,		stage_names=self.stage_names     )


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					version.parse("1.11"   )



				@property
				def   lowerCAmelCase__		(						self     ):
								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ]     )






				@property
				def   lowerCAmelCase__		(						self     ):
								return 1e-4




"""simple docstring"""






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

import pytest
from parameterized import 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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=1     ):
								# 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}-single'''       ,		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}       ,		metric_definitions=self.env.metric_definitions       ,		py_version="py36"       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								TrainingJobAnalytics(UpperCAmelCase__     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )






				def   lowerCAmelCase__		(						self     ):
								# create estimator
								SCREAMING_SNAKE_CASE__     							=				self.create_estimator()

								# run training
								estimator.fit()

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

								# extract kpis
								SCREAMING_SNAKE_CASE__     							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
								SCREAMING_SNAKE_CASE__     							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								SCREAMING_SNAKE_CASE__     							=				(
								    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"       ,		99_9999     )
								)

								# 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__     )
