





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[Any]						,__A	:	Union[str, Any]						,__A	:	Tuple=7						,__A	:	List[Any]=3						,__A	:	int=30						,__A	:	int=400						,__A	:	Any=True						,__A	:	Dict=None						,__A	:	Any=True						,__A	:	Dict=[0.5, 0.5, 0.5]						,__A	:	Union[str, Any]=[0.5, 0.5, 0.5]						,__A	:	int=True						,__A	:	List[str]=1 / 255						,__A	:	List[Any]=True						,)						->					Union[str, Any]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         _lowercase							= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= num_channels
         _lowercase							= min_resolution
         _lowercase							= max_resolution
         _lowercase							= do_resize
         _lowercase							= size
         _lowercase							= do_normalize
         _lowercase							= image_mean
         _lowercase							= image_std
         _lowercase							= do_rescale
         _lowercase							= rescale_factor
         _lowercase							= do_pad







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }







       def    __UpperCAmelCase (    self	:	Any						,__A	:	Union[str, Any]						,__A	:	Union[str, Any]=False  )						->					Dict:
         if not batched:
           _lowercase							= image_inputs[0]
           if isinstance(__A						,Image.Image  ):
             _lowercase						,		_lowercase							= image.size
           else:
             _lowercase						,		_lowercase							= image.shape[1], image.shape[2]
           if w < h:
             _lowercase							= int(self.size['shortest_edge'] * h / w  )
             _lowercase							= self.size['shortest_edge']
           elif w > h:
             _lowercase							= self.size['shortest_edge']
             _lowercase							= int(self.size['shortest_edge'] * w / h  )
           else:
             _lowercase							= self.size['shortest_edge']
             _lowercase							= self.size['shortest_edge']

         else:
           _lowercase							= []
           for image in image_inputs:
             _lowercase						,		_lowercase							= self.get_expected_values([image]  )
             expected_values.append((expected_height, expected_width)  )
           _lowercase							= max(__A						,key=lambda __A  : item[0]  )[0]
           _lowercase							= max(__A						,key=lambda __A  : item[1]  )[1]

         return expected_height, expected_width





@require_torch
@require_vision
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  YolosImageProcessor if is_vision_available() else None







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Dict:
         _lowercase							= YolosImageProcessingTester(self  )







       @property
       def    __UpperCAmelCase (    self	:	Any  )						->					int:
         return self.image_processor_tester.prepare_image_processor_dict()







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Any:
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(__A						,'image_mean'  )  )
         self.assertTrue(hasattr(__A						,'image_std'  )  )
         self.assertTrue(hasattr(__A						,'do_normalize'  )  )
         self.assertTrue(hasattr(__A						,'do_resize'  )  )
         self.assertTrue(hasattr(__A						,'size'  )  )







       def    __UpperCAmelCase (    self	:	str  )						->					Dict:
         _lowercase							= self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size						,{'shortest_edge': 18, 'longest_edge': 1333}  )
         self.assertEqual(image_processor.do_pad						,__A  )

         _lowercase							= self.image_processing_class.from_dict(
             self.image_processor_dict						,size=42						,max_size=84						,pad_and_return_pixel_mask=__A  )
         self.assertEqual(image_processor.size						,{'shortest_edge': 42, 'longest_edge': 84}  )
         self.assertEqual(image_processor.do_pad						,__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					str:
         pass







       def    __UpperCAmelCase (    self	:	str  )						->					List[Any]:
         # Initialize image_processing
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         _lowercase							= prepare_image_inputs(self.image_processor_tester						,equal_resolution=__A  )
         for image in image_inputs:
           self.assertIsInstance(__A						,Image.Image  )

         # Test not batched input
         _lowercase							= image_processing(image_inputs[0]						,return_tensors='pt'  ).pixel_values

         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A  )

         self.assertEqual(
             encoded_images.shape						,(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,)

         # Test batched
         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A						,batched=__A  )

         _lowercase							= image_processing(__A						,return_tensors='pt'  ).pixel_values
         self.assertEqual(
             encoded_images.shape						,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,)







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Optional[Any]:
         # Initialize image_processing
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         _lowercase							= prepare_image_inputs(self.image_processor_tester						,equal_resolution=__A						,numpify=__A  )
         for image in image_inputs:
           self.assertIsInstance(__A						,np.ndarray  )

         # Test not batched input
         _lowercase							= image_processing(image_inputs[0]						,return_tensors='pt'  ).pixel_values

         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A  )

         self.assertEqual(
             encoded_images.shape						,(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,)

         # Test batched
         _lowercase							= image_processing(__A						,return_tensors='pt'  ).pixel_values

         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A						,batched=__A  )

         self.assertEqual(
             encoded_images.shape						,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,)







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         # Initialize image_processing
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         _lowercase							= prepare_image_inputs(self.image_processor_tester						,equal_resolution=__A						,torchify=__A  )
         for image in image_inputs:
           self.assertIsInstance(__A						,torch.Tensor  )

         # Test not batched input
         _lowercase							= image_processing(image_inputs[0]						,return_tensors='pt'  ).pixel_values

         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A  )

         self.assertEqual(
             encoded_images.shape						,(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,)

         # Test batched
         _lowercase							= image_processing(__A						,return_tensors='pt'  ).pixel_values

         _lowercase						,		_lowercase							= self.image_processor_tester.get_expected_values(__A						,batched=__A  )

         self.assertEqual(
             encoded_images.shape						,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,)







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[Any]:
         # Initialize image_processings
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         _lowercase							= self.image_processing_class(do_resize=__A						,do_normalize=__A						,do_rescale=__A  )
         # create random PyTorch tensors
         _lowercase							= prepare_image_inputs(self.image_processor_tester						,equal_resolution=__A						,torchify=__A  )
         for image in image_inputs:
           self.assertIsInstance(__A						,torch.Tensor  )

         # Test whether the method "pad" and calling the image processor return the same tensors
         _lowercase							= image_processing_a.pad(__A						,return_tensors='pt'  )
         _lowercase							= image_processing_a(__A						,return_tensors='pt'  )

         self.assertTrue(
             torch.allclose(encoded_images_with_method['pixel_values']						,encoded_images['pixel_values']						,atol=1e-4  )  )







       @slow
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Union[str, Any]:
         # prepare image and target
         _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,'r'  ) as f:
           _lowercase							= json.loads(f.read()  )

         _lowercase							= {'image_id': 3_9769, 'annotations': target}

         # encode them
         _lowercase							= YolosImageProcessor.from_pretrained('hustvl/yolos-small'  )
         _lowercase							= image_processing(images=__A						,annotations=__A						,return_tensors='pt'  )

         # verify pixel values
         _lowercase							= torch.Size([1, 3, 800, 1066]  )
         self.assertEqual(encoding['pixel_values'].shape						,__A  )

         _lowercase							= torch.tensor([0.2796, 0.3138, 0.3481]  )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,__A						,atol=1e-4  )  )

         # verify area
         _lowercase							= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,__A  )  )
         # verify boxes
         _lowercase							= torch.Size([6, 4]  )
         self.assertEqual(encoding['labels'][0]['boxes'].shape						,__A  )
         _lowercase							= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,__A						,atol=1e-3  )  )
         # verify image_id
         _lowercase							= torch.tensor([3_9769]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,__A  )  )
         # verify is_crowd
         _lowercase							= torch.tensor([0, 0, 0, 0, 0, 0]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,__A  )  )
         # verify class_labels
         _lowercase							= torch.tensor([75, 75, 63, 65, 17, 17]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,__A  )  )
         # verify orig_size
         _lowercase							= torch.tensor([480, 640]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,__A  )  )
         # verify size
         _lowercase							= torch.tensor([800, 1066]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,__A  )  )







       @slow
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict:
         # prepare image, target and masks_path
         _lowercase							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,'r'  ) as f:
           _lowercase							= json.loads(f.read()  )

         _lowercase							= {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

         _lowercase							= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

         # encode them
         _lowercase							= YolosImageProcessor(format='coco_panoptic'  )
         _lowercase							= image_processing(images=__A						,annotations=__A						,masks_path=__A						,return_tensors='pt'  )

         # verify pixel values
         _lowercase							= torch.Size([1, 3, 800, 1066]  )
         self.assertEqual(encoding['pixel_values'].shape						,__A  )

         _lowercase							= torch.tensor([0.2796, 0.3138, 0.3481]  )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,__A						,atol=1e-4  )  )

         # verify area
         _lowercase							= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,__A  )  )
         # verify boxes
         _lowercase							= torch.Size([6, 4]  )
         self.assertEqual(encoding['labels'][0]['boxes'].shape						,__A  )
         _lowercase							= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,__A						,atol=1e-3  )  )
         # verify image_id
         _lowercase							= torch.tensor([3_9769]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,__A  )  )
         # verify is_crowd
         _lowercase							= torch.tensor([0, 0, 0, 0, 0, 0]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,__A  )  )
         # verify class_labels
         _lowercase							= torch.tensor([17, 17, 63, 75, 75, 93]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,__A  )  )
         # verify masks
         _lowercase							= 82_2873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,__A  )
         # verify orig_size
         _lowercase							= torch.tensor([480, 640]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,__A  )  )
         # verify size
         _lowercase							= torch.tensor([800, 1066]  )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,__A  )  )






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 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	List[Any]						,__A	:	int						,__A	:	str=13						,__A	:	str=7						,__A	:	Optional[Any]=True						,__A	:	Optional[int]=True						,__A	:	int=True						,__A	:	List[str]=True						,__A	:	Dict=99						,__A	:	Dict=32						,__A	:	Dict=5						,__A	:	Dict=4						,__A	:	int=37						,__A	:	Any="gelu"						,__A	:	int=0.1						,__A	:	int=0.1						,__A	:	Dict=512						,__A	:	List[str]=16						,__A	:	Tuple=2						,__A	:	Dict=0.02						,__A	:	int=4						,)						->					Optional[int]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= seq_length
         _lowercase							= is_training
         _lowercase							= use_attention_mask
         _lowercase							= use_token_type_ids
         _lowercase							= use_labels
         _lowercase							= vocab_size
         _lowercase							= hidden_size
         _lowercase							= num_hidden_layers
         _lowercase							= num_attention_heads
         _lowercase							= intermediate_size
         _lowercase							= hidden_act
         _lowercase							= hidden_dropout_prob
         _lowercase							= attention_probs_dropout_prob
         _lowercase							= max_position_embeddings
         _lowercase							= type_vocab_size
         _lowercase							= type_sequence_label_size
         _lowercase							= initializer_range
         _lowercase							= num_choices







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

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

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

         _lowercase							= 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=__A						,initializer_range=self.initializer_range						,)

         return config, input_ids, token_type_ids, attention_mask







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Tuple:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs
         _lowercase							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         _lowercase							= self.prepare_config_and_inputs()
         _lowercase						,		_lowercase						,		_lowercase						,		_lowercase							= config_and_inputs

         _lowercase							= True
         _lowercase							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         _lowercase							= 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 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[int]      =  True

       SCREAMING_SNAKE_CASE_      : Dict      =  (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= FlaxRobertaPreLayerNormModelTester(self  )







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Dict:
         for model_class_name in self.all_model_classes:
           _lowercase							= model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )
           _lowercase							= model(np.ones((1, 1)  )  )
           self.assertIsNotNone(__A  )





@require_flax
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @slow
       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         _lowercase							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         _lowercase							= [1, 11, 5_0265]
         self.assertEqual(list(output.shape  )						,__A  )
         # compare the actual values for a slice.
         _lowercase							= 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]						,__A						,atol=1e-4  )  )







       @slow
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         _lowercase							= FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,from_pt=__A  )

         _lowercase							= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						,dtype=jnp.intaa  )
         _lowercase							= model(__A  )[0]
         # compare the actual values for a slice.
         _lowercase							= 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]						,__A						,atol=1e-4  )  )