



'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor



class    lowerCamelCase__				(			unittest.TestCase						):






      """simple docstring"""




      def __init__(       self							:     List[str]			,a__							:     int			,a__							:     str=7			,a__							:     List[str]=3			,a__							:     Any=18			,a__							:     List[Any]=30			,a__							:     Any=4_00			,a__							:     Dict=True			,a__							:     List[str]=None			,a__							:     Optional[Any]=True			,a__							:     Tuple=None			,a__							:     Optional[int]=True			,a__							:     List[Any]=[0.5, 0.5, 0.5]			,a__							:     List[Any]=[0.5, 0.5, 0.5]			,):
             a__								= size if size is not None else {"shortest_edge": 18}
             a__								= crop_size if crop_size is not None else {"height": 18, "width": 18}
             a__								= parent
             a__								= batch_size
             a__								= num_channels
             a__								= image_size
             a__								= min_resolution
             a__								= max_resolution
             a__								= do_resize
             a__								= size
             a__								= do_center_crop
             a__								= crop_size
             a__								= do_normalize
             a__								= image_mean
             a__								= image_std




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "do_center_crop": self.do_center_crop,
                 "size": self.size,
                 "crop_size": self.crop_size,
             }



@require_torch
@require_vision
class    lowerCamelCase__				(			__lowerCAmelCase	,    unittest.TestCase						):






      """simple docstring"""
      UpperCamelCase__			 =     LevitImageProcessor if is_vision_available() else None




      def lowerCAmelCase_					(       self							:     Dict   ):
             a__								= LevitImageProcessingTester(self   )




      @property
      def lowerCAmelCase_					(       self							:     int   ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def lowerCAmelCase_					(       self							:     List[Any]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"image_mean"   )   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"image_std"   )   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"do_normalize"   )   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"do_resize"   )   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"do_center_crop"   )   )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			,"size"   )   )




      def lowerCAmelCase_					(       self							:     List[str]   ):
             a__								= self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size			,{"shortest_edge": 18}   )
             self.assertEqual(image_processor.crop_size			,{"height": 18, "width": 18}   )

             a__								= self.image_processing_class.from_dict(self.image_processor_dict			,size=42			,crop_size=84   )
             self.assertEqual(image_processor.size			,{"shortest_edge": 42}   )
             self.assertEqual(image_processor.crop_size			,{"height": 84, "width": 84}   )




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             pass




      def lowerCAmelCase_					(       self							:     Any   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=__SCREAMING_SNAKE_CASE   )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE			,Image.Image   )

             # Test not batched input
             a__								= image_processing(image_inputs[0]			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)

             # Test batched
             a__								= image_processing(__SCREAMING_SNAKE_CASE			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=__SCREAMING_SNAKE_CASE			,numpify=__SCREAMING_SNAKE_CASE   )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE			,np.ndarray   )

             # Test not batched input
             a__								= image_processing(image_inputs[0]			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)

             # Test batched
             a__								= image_processing(__SCREAMING_SNAKE_CASE			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)




      def lowerCAmelCase_					(       self							:     Optional[Any]   ):
             a__								= self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             a__								= prepare_image_inputs(self.image_processor_tester			,equal_resolution=__SCREAMING_SNAKE_CASE			,torchify=__SCREAMING_SNAKE_CASE   )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE			,torch.Tensor   )

             # Test not batched input
             a__								= image_processing(image_inputs[0]			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)

             # Test batched
             a__								= image_processing(__SCREAMING_SNAKE_CASE			,return_tensors="pt"   ).pixel_values
             self.assertEqual(
                 encoded_images.shape			,(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )			,)






'''simple docstring'''



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.roformer.modeling_flax_roformer import (
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerModel,
     )





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=13     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=99     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=37     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE=512     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=2     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=4     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	seq_length
            __snake_case            =	is_training
            __snake_case            =	use_attention_mask
            __snake_case            =	use_token_type_ids
            __snake_case            =	use_labels
            __snake_case            =	vocab_size
            __snake_case            =	hidden_size
            __snake_case            =	num_hidden_layers
            __snake_case            =	num_attention_heads
            __snake_case            =	intermediate_size
            __snake_case            =	hidden_act
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	max_position_embeddings
            __snake_case            =	type_vocab_size
            __snake_case            =	type_sequence_label_size
            __snake_case            =	initializer_range
            __snake_case            =	num_choices


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



            '''simple docstring'''




            __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size	)

            __snake_case            =	None
            if self.use_attention_mask:
                 __snake_case            =	random_attention_mask([self.batch_size, self.seq_length]	)

            __snake_case            =	None
            if self.use_token_type_ids:
                 __snake_case            =	ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size	)

            __snake_case            =	RoFormerConfig(
                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=__SCREAMING_SNAKE_CASE     ,	initializer_range=self.initializer_range     ,	)

            return config, input_ids, token_type_ids, attention_mask




       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.prepare_config_and_inputs()
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	config_and_inputs
            __snake_case            =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
            return config, inputs_dict





@require_flax
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      True

       __lowercase	:   Optional[int]										=      (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerModelTester(self	)




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



            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __snake_case            =	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''     ,	from_pt=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(np.ones((1, 1)	)	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)





@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @slow
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''	)
            __snake_case            =	jnp.array([[0, 1, 2, 3, 4, 5]]	)
            __snake_case            =	model(__SCREAMING_SNAKE_CASE	)[0]

            __snake_case            =	5_0000

            __snake_case            =	(1, 6, vocab_size)
            self.assertEqual(output.shape     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	jnp.array(
                [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]	)

            self.assertTrue(jnp.allclose(output[:, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-4	)	)






