

"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Optional[int]		):
						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=lowercase_   ,)
						assert hasattr(self   ,'''env'''		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Dict=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=lowercase_   ,instance_type=self.instance_type   ,debugger_hook_config=lowercase_   ,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			:				Dict   ,lowercase_			:				Optional[int]		):
						TrainingJobAnalytics(lowercase_		).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'		)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						# create estimator
						lowerCAmelCase__	:						Any  				=	self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						lowerCAmelCase__	:						Any  				=	TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

						# extract kpis
						lowerCAmelCase__	:						Any  				=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
						lowerCAmelCase__	:						str  				=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						lowerCAmelCase__	:						int  				=	(
						    Session().describe_training_job(estimator.latest_training_job.name		).get('''TrainingTimeInSeconds'''   ,9_9_9_9_9_9		)
						)

						# 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}   ,lowercase_		)







"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
							import jax

							from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				List[str]   ,lowercase_			:				Any=1_0_0   ,lowercase_			:				str=1_3   ,lowercase_			:				Any=3_0   ,lowercase_			:				Optional[int]=2   ,lowercase_			:				Dict=3   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Union[str, Any]=True   ,lowercase_			:				Optional[Any]=3_2   ,lowercase_			:				List[Any]=5   ,lowercase_			:				Any=4   ,lowercase_			:				Optional[int]=3_7   ,lowercase_			:				List[Any]="gelu"   ,lowercase_			:				Dict=0.1   ,lowercase_			:				List[Any]=0.1   ,lowercase_			:				int=1_0   ,lowercase_			:				int=0.02   ,lowercase_			:				List[str]=3   ,):
						lowerCAmelCase__	:						int  				=	parent
						lowerCAmelCase__	:						Dict  				=	vocab_size
						lowerCAmelCase__	:						List[Any]  				=	batch_size
						lowerCAmelCase__	:						Union[str, Any]  				=	image_size
						lowerCAmelCase__	:						Optional[int]  				=	patch_size
						lowerCAmelCase__	:						str  				=	num_channels
						lowerCAmelCase__	:						str  				=	is_training
						lowerCAmelCase__	:						Optional[Any]  				=	use_labels
						lowerCAmelCase__	:						Union[str, Any]  				=	hidden_size
						lowerCAmelCase__	:						Optional[Any]  				=	num_hidden_layers
						lowerCAmelCase__	:						Any  				=	num_attention_heads
						lowerCAmelCase__	:						Union[str, Any]  				=	intermediate_size
						lowerCAmelCase__	:						List[Any]  				=	hidden_act
						lowerCAmelCase__	:						str  				=	hidden_dropout_prob
						lowerCAmelCase__	:						List[Any]  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						Union[str, Any]  				=	type_sequence_label_size
						lowerCAmelCase__	:						Dict  				=	initializer_range

						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						lowerCAmelCase__	:						Any  				=	(image_size // patch_size) ** 2
						lowerCAmelCase__	:						str  				=	num_patches + 1







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						lowerCAmelCase__	:						Optional[int]  				=	None
						if self.use_labels:
							lowerCAmelCase__	:						str  				=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size		)

						lowerCAmelCase__	:						List[str]  				=	BeitConfig(
						    vocab_size=self.vocab_size   ,image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,is_decoder=lowercase_   ,initializer_range=self.initializer_range   ,)

						return config, pixel_values, labels







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				str   ,lowercase_			:				Dict   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Any  				=	FlaxBeitModel(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	FlaxBeitForMaskedImageModeling(config=lowercase_		)
						lowerCAmelCase__	:						str  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length - 1, self.vocab_size)		)







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				str   ,lowercase_			:				List[str]   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Dict  				=	self.type_sequence_label_size
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(config=lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						lowerCAmelCase__	:						int  				=	1
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification(lowercase_		)

						lowerCAmelCase__	:						str  				=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						lowerCAmelCase__	:						List[str]  				=	model(lowercase_		)







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







@require_flax
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       (
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitModelTester(self		)
						lowerCAmelCase__	:						str  				=	ConfigTester(self   ,config_class=lowercase_   ,has_text_modality=lowercase_   ,hidden_size=3_7		)







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







					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(lowercase_		)
							lowerCAmelCase__	:						Optional[int]  				=	inspect.signature(model.__call__		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__	:						Tuple  				=	[*signature.parameters.keys()]

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







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

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
								lowerCAmelCase__	:						Union[str, Any]  				=	self._prepare_for_class(lowercase_   ,lowercase_		)
								lowerCAmelCase__	:						Dict  				=	model_class(lowercase_		)

								@jax.jit
								def model_jitted(lowercase_			:				Dict   ,**lowercase_			:				List[Any]		):
									return model(pixel_values=lowercase_   ,**lowercase_		)

								with self.subTest('''JIT Enabled'''		):
									lowerCAmelCase__	:						Any  				=	model_jitted(**lowercase_		).to_tuple()

								with self.subTest('''JIT Disabled'''		):
									with jax.disable_jit():
										lowerCAmelCase__	:						int  				=	model_jitted(**lowercase_		).to_tuple()

								self.assertEqual(len(lowercase_		)   ,len(lowercase_		)		)
								for jitted_output, output in zip(lowercase_   ,lowercase_		):
									self.assertEqual(jitted_output.shape   ,output.shape		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[int]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowercase_		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[str]		):
						for model_class_name in self.all_model_classes:
							lowerCAmelCase__	:						Tuple  				=	model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''		)
							lowerCAmelCase__	:						List[Any]  				=	model(np.ones((1, 3, 2_2_4, 2_2_4)		)		)
							self.assertIsNotNone(lowercase_		)

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







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






					"""simple docstring"""



					@cached_property
					def        __lowerCAmelCase (       self			:				Dict		):
						return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''		) if is_vision_available() else None







					@slow
					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''		)

						lowerCAmelCase__	:						List[str]  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[int]  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		).pixel_values

						# prepare bool_masked_pos
						lowerCAmelCase__	:						str  				=	np.ones((1, 1_9_6)   ,dtype=lowercase_		)

						# forward pass
						lowerCAmelCase__	:						Dict  				=	model(pixel_values=lowercase_   ,bool_masked_pos=lowercase_		)
						lowerCAmelCase__	:						str  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Any  				=	(1, 1_9_6, 8_1_9_2)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						str  				=	np.array(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

						self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]   ,lowercase_   ,atol=1E-2		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[str]  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''		)

						lowerCAmelCase__	:						Dict  				=	self.default_image_processor
						lowerCAmelCase__	:						Optional[Any]  				=	prepare_img()
						lowerCAmelCase__	:						Any  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						List[Any]  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Dict  				=	(1, 1_0_0_0)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Dict  				=	np.array([-1.2385, -1.0987, -1.0108]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Union[str, Any]  				=	2_8_1
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)







					@slow
					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.default_image_processor
						lowerCAmelCase__	:						str  				=	prepare_img()
						lowerCAmelCase__	:						Union[str, Any]  				=	image_processor(images=lowercase_   ,return_tensors='''np'''		)

						# forward pass
						lowerCAmelCase__	:						Tuple  				=	model(**lowercase_		)
						lowerCAmelCase__	:						int  				=	outputs.logits

						# verify the logits
						lowerCAmelCase__	:						Optional[int]  				=	(1, 2_1_8_4_1)
						self.assertEqual(logits.shape   ,lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	np.array([1.6881, -0.2787, 0.5901]		)

						self.assertTrue(np.allclose(logits[0, :3]   ,lowercase_   ,atol=1E-4		)		)

						lowerCAmelCase__	:						Optional[int]  				=	2_3_9_6
						self.assertEqual(logits.argmax(-1		).item()   ,lowercase_		)




