





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class  _lowerCamelCase       (			unittest.TestCase			):



      """simple docstring"""






      def __init__(       self ,  UpperCAmelCase ,  UpperCAmelCase=13 ,  UpperCAmelCase=30 ,  UpperCAmelCase=2 ,  UpperCAmelCase=3 ,  UpperCAmelCase=True ,  UpperCAmelCase=True ,  UpperCAmelCase=32 ,  UpperCAmelCase=5 ,  UpperCAmelCase=4 ,  UpperCAmelCase=37 ,  UpperCAmelCase="gelu" ,  UpperCAmelCase=0.1 ,  UpperCAmelCase=0.1 ,  UpperCAmelCase=10 ,  UpperCAmelCase=0.02 ,  )				->      str:






        '''simple docstring'''






        __snake_case      : List[Any]						=				parent
        __snake_case      : List[str]						=				batch_size
        __snake_case      : Tuple						=				image_size
        __snake_case      : Any						=				patch_size
        __snake_case      : Union[str, Any]						=				num_channels
        __snake_case      : Dict						=				is_training
        __snake_case      : Optional[int]						=				use_labels
        __snake_case      : Optional[Any]						=				hidden_size
        __snake_case      : List[Any]						=				num_hidden_layers
        __snake_case      : Union[str, Any]						=				num_attention_heads
        __snake_case      : Dict						=				intermediate_size
        __snake_case      : str						=				hidden_act
        __snake_case      : List[str]						=				hidden_dropout_prob
        __snake_case      : List[Any]						=				attention_probs_dropout_prob
        __snake_case      : Tuple						=				type_sequence_label_size
        __snake_case      : int						=				initializer_range

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







      def   UpperCAmelCase						(       self      )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : Dict						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        __snake_case      : Union[str, Any]						=				ViTConfig(
            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=_A ,  initializer_range=self.initializer_range ,  )

        return config, pixel_values







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase      )				->      int:






        '''simple docstring'''






        __snake_case      : int						=				FlaxViTModel(config=_A      )
        __snake_case      : Any						=				model(_A      )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        __snake_case      : List[Any]						=				(self.image_size, self.image_size)
        __snake_case      : Tuple						=				(self.patch_size, self.patch_size)
        __snake_case      : List[Any]						=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, num_patches + 1, self.hidden_size)      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase      )				->      List[str]:






        '''simple docstring'''






        __snake_case      : int						=				self.type_sequence_label_size
        __snake_case      : Tuple						=				FlaxViTForImageClassification(config=_A      )
        __snake_case      : List[str]						=				model(_A      )
        self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)      )

        # test greyscale images
        __snake_case      : Union[str, Any]						=				1
        __snake_case      : List[str]						=				FlaxViTForImageClassification(_A      )

        __snake_case      : Optional[int]						=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
        __snake_case      : Tuple						=				model(_A      )







      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        __snake_case      : str						=				self.prepare_config_and_inputs()
        (
            (
            __snake_case			
        )			,      (
            __snake_case			
        )			,      			
        )      : List[str]						=				config_and_inputs
        __snake_case      : Dict						=				{"pixel_values": pixel_values}
        return config, inputs_dict




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



      """simple docstring"""






      UpperCAmelCase_  :					Tuple      			=(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







      def   UpperCAmelCase						(       self      )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : List[Any]						=				FlaxViTModelTester(self      )
        __snake_case      : List[Any]						=				ConfigTester(self ,  config_class=_A ,  has_text_modality=_A ,  hidden_size=37      )







      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        self.config_tester.run_common_tests()







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case      : Optional[Any]						=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_A      )







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_A      )







      def   UpperCAmelCase						(       self      )				->      Optional[int]:






        '''simple docstring'''






        __snake_case			,      __snake_case      : Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __snake_case      : str						=				model_class(_A      )
          __snake_case      : Optional[int]						=				inspect.signature(model.__call__      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __snake_case      : str						=				[*signature.parameters.keys()]

          __snake_case      : int						=				["pixel_values"]
          self.assertListEqual(arg_names[:1] ,  _A      )







      def   UpperCAmelCase						(       self      )				->      str:






        '''simple docstring'''






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

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__      ):
            __snake_case      : Union[str, Any]						=				self._prepare_for_class(_A ,  _A      )
            __snake_case      : int						=				model_class(_A      )

            @jax.jit
            def model_jitted(UpperCAmelCase ,  **UpperCAmelCase      ):
              return model(pixel_values=_A ,  **_A      )

            with self.subTest("JIT Enabled"      ):
              __snake_case      : List[Any]						=				model_jitted(**_A      ).to_tuple()

            with self.subTest("JIT Disabled"      ):
              with jax.disable_jit():
                __snake_case      : int						=				model_jitted(**_A      ).to_tuple()

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







      @slow
      def   UpperCAmelCase						(       self      )				->      Optional[Any]:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
          __snake_case      : Tuple						=				model_class_name.from_pretrained("google/vit-base-patch16-224"      )
          __snake_case      : int						=				model(np.ones((1, 3, 224, 224)      )      )
          self.assertIsNotNone(_A      )








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''': 6_5_0, '''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''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]			)
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				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=_A							,					)
				assert hasattr(self							,					'''env'''		)
	def      _lowercase				(    self							,					_A=1		):






				'''simple docstring'''







				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=_A							,					instance_type=self.instance_type							,					debugger_hook_config=_A							,					hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}							,					metric_definitions=self.env.metric_definitions							,					py_version='''py36'''							,					)
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				TrainingJobAnalytics(_A		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.create_estimator()

				# run training
				estimator.fit()

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

				# extract kpis
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']		)
				UpperCAmelCase   		=				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']		)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				UpperCAmelCase   		=				(
				    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}							,					_A		)



