'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
  )


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=13       ,lowerCamelCase_=7       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=99       ,lowerCamelCase_=32       ,lowerCamelCase_=5       ,lowerCamelCase_=4       ,lowerCamelCase_=37       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=16       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=4       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	parent
         UpperCAmelCase__				:					List[Any] 					=	batch_size
         UpperCAmelCase__				:					Optional[int] 					=	seq_length
         UpperCAmelCase__				:					Optional[Any] 					=	is_training
         UpperCAmelCase__				:					Optional[int] 					=	use_attention_mask
         UpperCAmelCase__				:					Any 					=	use_token_type_ids
         UpperCAmelCase__				:					Optional[Any] 					=	use_labels
         UpperCAmelCase__				:					List[str] 					=	vocab_size
         UpperCAmelCase__				:					str 					=	hidden_size
         UpperCAmelCase__				:					Union[str, Any] 					=	num_hidden_layers
         UpperCAmelCase__				:					Dict 					=	num_attention_heads
         UpperCAmelCase__				:					Optional[int] 					=	intermediate_size
         UpperCAmelCase__				:					Any 					=	hidden_act
         UpperCAmelCase__				:					List[str] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					List[Any] 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					str 					=	max_position_embeddings
         UpperCAmelCase__				:					List[Any] 					=	type_vocab_size
         UpperCAmelCase__				:					List[Any] 					=	type_sequence_label_size
         UpperCAmelCase__				:					Dict 					=	initializer_range
         UpperCAmelCase__				:					Tuple 					=	num_choices




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



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size				)

         UpperCAmelCase__				:					int 					=	None
         if self.use_attention_mask:
            UpperCAmelCase__				:					Union[str, Any] 					=	random_attention_mask([self.batch_size, self.seq_length]				)

         UpperCAmelCase__				:					Optional[Any] 					=	None
         if self.use_token_type_ids:
            UpperCAmelCase__				:					Dict 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size				)

         UpperCAmelCase__				:					Union[str, Any] 					=	RobertaPreLayerNormConfig(
             vocab_size=self.vocab_size       ,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       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,is_decoder=lowerCamelCase_       ,initializer_range=self.initializer_range       ,)

         return config, input_ids, token_type_ids, attention_mask




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	config_and_inputs
         UpperCAmelCase__				:					List[Any] 					=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Any 					=	config_and_inputs

         UpperCAmelCase__				:					Optional[Any] 					=	True
         UpperCAmelCase__				:					Optional[int] 					=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
         UpperCAmelCase__				:					Any 					=	ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2				)

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= True

      UpperCAmelCase_   :   Union[str, Any]   				= (
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	FlaxRobertaPreLayerNormModelTester(self				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         for model_class_name in self.all_model_classes:
            UpperCAmelCase__				:					Optional[int] 					=	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,from_pt=lowerCamelCase_				)
            UpperCAmelCase__				:					List[Any] 					=	model(np.ones((1, 1)				)				)
            self.assertIsNotNone(lowerCamelCase_				)




@require_flax
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,from_pt=lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[int] 					=	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,dtype=jnp.intaa				)
         UpperCAmelCase__				:					Any 					=	model(lowerCamelCase_				)[0]
         UpperCAmelCase__				:					Optional[Any] 					=	[1, 11, 50265]
         self.assertEqual(list(output.shape				)       ,lowerCamelCase_				)
         # compare the actual values for a slice.
         UpperCAmelCase__				:					int 					=	np.array(
             [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]       ,dtype=np.floataa				)
         self.assertTrue(np.allclose(output[:, :3, :3]       ,lowerCamelCase_       ,atol=1e-4				)				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,from_pt=lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,dtype=jnp.intaa				)
         UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_				)[0]
         # compare the actual values for a slice.
         UpperCAmelCase__				:					Union[str, Any] 					=	np.array(
             [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]       ,dtype=np.floataa				)
         self.assertTrue(np.allclose(output[:, :3, :3]       ,lowerCamelCase_       ,atol=1e-4				)				)





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase__  :						str					=      get_tests_dir('fixtures')


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	mock.Mock()
         UpperCAmelCase__				:					Dict 					=	500
         UpperCAmelCase__				:					Optional[int] 					=	{}
         UpperCAmelCase__				:					List[str] 					=	HTTPError
         UpperCAmelCase__				:					Tuple 					=	{}

         # Download this model to make sure it's in the cache.
         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch('''requests.Session.request'''       ,return_value=lowerCamelCase_				) as mock_head:
            UpperCAmelCase__				:					Tuple 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
            # This check we did call the fake head request
            mock_head.assert_called()




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	ViTImageProcessor.from_pretrained(
             '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''				)




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         with self.assertRaises(lowerCamelCase_				):
            # config is in subfolder, the following should not work without specifying the subfolder
            UpperCAmelCase__				:					int 					=	AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''				)

         UpperCAmelCase__				:					Optional[Any] 					=	AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/stable-diffusion-all-variants'''       ,subfolder='''feature_extractor'''				)

         self.assertIsNotNone(lowerCamelCase_				)




@is_staging_test
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @classmethod
      def 		lowerCAmelCase__       (					cls				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	TOKEN
         HfFolder.save_token(lowerCamelCase_				)




      @classmethod
      def 		lowerCAmelCase__       (					cls				)      ->      List[str]:



         '''simple docstring'''



         try:
            delete_repo(token=cls._token       ,repo_id='''test-image-processor'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''valid_org/test-image-processor-org'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''test-dynamic-image-processor'''				)
         except HTTPError:
            pass




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''test-image-processor'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					List[Any] 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''valid_org/test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''valid_org/test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''valid_org/test-image-processor-org'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         CustomImageProcessor.register_for_auto_class()
         UpperCAmelCase__				:					List[str] 					=	CustomImageProcessor.from_pretrained(lowerCamelCase_				)

         image_processor.push_to_hub('''test-dynamic-image-processor'''       ,use_auth_token=self._token				)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             image_processor.auto_map       ,{'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}       ,)

         UpperCAmelCase__				:					Any 					=	AutoImageProcessor.from_pretrained(
             f'''{USER}/test-dynamic-image-processor'''       ,trust_remote_code=lowerCamelCase_				)
         # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
         self.assertEqual(new_image_processor.__class__.__name__       ,'''CustomImageProcessor'''				)




