






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 GLPNImageProcessor




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=7       ,			a=3       ,			a=18       ,			a=30       ,			a=4_00       ,			a=True       ,			a=32       ,			a=True       ,			)       ->    Optional[Any]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							num_channels
								snake_case_				=							image_size
								snake_case_				=							min_resolution
								snake_case_				=							max_resolution
								snake_case_				=							do_resize
								snake_case_				=							size_divisor
								snake_case_				=							do_rescale






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}






@require_torch
@require_vision
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= GLPNImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							GLPNImageProcessingTester(self					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(a       ,			'do_resize'					)					)
								self.assertTrue(hasattr(a       ,			'size_divisor'					)					)
								self.assertTrue(hasattr(a       ,			'resample'					)					)
								self.assertTrue(hasattr(a       ,			'do_rescale'					)					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								pass




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			Image.Image					)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								snake_case_				=							image_processing(image_inputs[0]       ,			return_tensors='pt'					).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								snake_case_				=							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 (GLPNImageProcessor doesn't support batching)
								snake_case_				=							image_processing(image_inputs[0]       ,			return_tensors='pt'					).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)






	def 							_UpperCamelCase			(       self					)       ->    str:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								snake_case_				=							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 (GLPNImageProcessor doesn't support batching)
								snake_case_				=							image_processing(image_inputs[0]       ,			return_tensors='pt'					).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0					)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0					)













import unittest

import numpy as np

from transformers import RobertaConfig, 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():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								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 							_UpperCamelCase			(       self					)       ->    List[str]:
								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_				=							RobertaConfig(
								    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					)       ->    List[Any]:
								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






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

								snake_case_				=							True
								snake_case_				=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
								snake_case_				=							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
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= True

	lowerCAmelCase													= (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





