






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       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=7       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=99       ,  _UpperCamelCase=32       ,  _UpperCamelCase=5       ,  _UpperCamelCase=4       ,  _UpperCamelCase=37       ,  _UpperCamelCase="gelu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=5_12       ,  _UpperCamelCase=16       ,  _UpperCamelCase=2       ,  _UpperCamelCase=0.02       ,  _UpperCamelCase=4       ,  ):






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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

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

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

												lowerCAmelCase__        =  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=_UpperCamelCase       ,  initializer_range=self.initializer_range       ,  )

												return config, input_ids, token_type_ids, attention_mask







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()
												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  config_and_inputs
												lowerCAmelCase__        =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
												return config, inputs_dict




@require_flax
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    str						=   True

						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxRoFormerModelTester(self				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for model_class_name in self.all_model_classes:
																		lowerCAmelCase__        =  model_class_name.from_pretrained('junnyu/roformer_chinese_small'       ,  from_pt=_UpperCamelCase				)
																		lowerCAmelCase__        =  model(np.ones((1, 1)				)				)
																		self.assertIsNotNone(_UpperCamelCase				)




@require_flax
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'				)
												lowerCAmelCase__        =  jnp.array([[0, 1, 2, 3, 4, 5]]				)
												lowerCAmelCase__        =  model(_UpperCamelCase				)[0]

												lowerCAmelCase__        =  5_00_00

												lowerCAmelCase__        =  (1, 6, vocab_size)
												self.assertEqual(output.shape       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  jnp.array(
												    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]				)

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













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 DeformableDetrImageProcessor


class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=7       ,  _UpperCamelCase=3       ,  _UpperCamelCase=30       ,  _UpperCamelCase=4_00       ,  _UpperCamelCase=True       ,  _UpperCamelCase=None       ,  _UpperCamelCase=True       ,  _UpperCamelCase=[0.5, 0.5, 0.5]       ,  _UpperCamelCase=[0.5, 0.5, 0.5]       ,  _UpperCamelCase=True       ,  _UpperCamelCase=1 / 2_55       ,  _UpperCamelCase=True       ,  ):






												"""simple docstring"""
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  min_resolution
												lowerCAmelCase__        =  max_resolution
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean
												lowerCAmelCase__        =  image_std
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_pad






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												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       ,  _UpperCamelCase       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												if not batched:
																		lowerCAmelCase__        =  image_inputs[0]
																		if isinstance(_UpperCamelCase       ,  Image.Image				):
																								lowerCAmelCase__  ,    lowerCAmelCase__        =  image.size
																		else:
																								lowerCAmelCase__  ,    lowerCAmelCase__        =  image.shape[1], image.shape[2]
																		if w < h:
																								lowerCAmelCase__        =  int(self.size['shortest_edge'] * h / w				)
																								lowerCAmelCase__        =  self.size['shortest_edge']
																		elif w > h:
																								lowerCAmelCase__        =  self.size['shortest_edge']
																								lowerCAmelCase__        =  int(self.size['shortest_edge'] * w / h				)
																		else:
																								lowerCAmelCase__        =  self.size['shortest_edge']
																								lowerCAmelCase__        =  self.size['shortest_edge']

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

												return expected_height, expected_width




@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Dict						=   DeformableDetrImageProcessor if is_vision_available() else None






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  DeformableDetrImageProcessingTester(self				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.image_processor_tester.prepare_image_processor_dict()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'image_mean'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'image_std'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'do_normalize'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'do_resize'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'do_rescale'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'do_pad'				)				)
												self.assertTrue(hasattr(_UpperCamelCase       ,  'size'				)				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.image_processing_class.from_dict(self.image_processor_dict				)
												self.assertEqual(image_processor.size       ,  {'shortest_edge': 18, 'longest_edge': 13_33}				)
												self.assertEqual(image_processor.do_pad       ,  _UpperCamelCase				)

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												lowerCAmelCase__        =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_UpperCamelCase				)
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase       ,  Image.Image				)

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

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase				)

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

												# Test batched
												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase       ,  batched=_UpperCamelCase				)

												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  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				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												lowerCAmelCase__        =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_UpperCamelCase       ,  numpify=_UpperCamelCase				)
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase       ,  np.ndarray				)

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

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase				)

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

												# Test batched
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase       ,  batched=_UpperCamelCase				)

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												lowerCAmelCase__        =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_UpperCamelCase       ,  torchify=_UpperCamelCase				)
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase       ,  torch.Tensor				)

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

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase				)

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

												# Test batched
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  self.image_processor_tester.get_expected_values(_UpperCamelCase       ,  batched=_UpperCamelCase				)

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






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image and target
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

												lowerCAmelCase__        =  {'image_id': 3_97_69, 'annotations': target}

												# encode them
												lowerCAmelCase__        =  DeformableDetrImageProcessor()
												lowerCAmelCase__        =  image_processing(images=_UpperCamelCase       ,  annotations=_UpperCamelCase       ,  return_tensors='pt'				)

												# verify pixel values
												lowerCAmelCase__        =  torch.Size([1, 3, 8_00, 10_66]				)
												self.assertEqual(encoding['pixel_values'].shape       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  _UpperCamelCase       ,  atol=1E-4				)				)

												# verify area
												lowerCAmelCase__        =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  _UpperCamelCase				)				)
												# verify boxes
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([75, 75, 63, 65, 17, 17]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image, target and masks_path
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

												lowerCAmelCase__        =  {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

												# encode them
												lowerCAmelCase__        =  DeformableDetrImageProcessor(format='coco_panoptic'				)
												lowerCAmelCase__        =  image_processing(images=_UpperCamelCase       ,  annotations=_UpperCamelCase       ,  masks_path=_UpperCamelCase       ,  return_tensors='pt'				)

												# verify pixel values
												lowerCAmelCase__        =  torch.Size([1, 3, 8_00, 10_66]				)
												self.assertEqual(encoding['pixel_values'].shape       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  _UpperCamelCase       ,  atol=1E-4				)				)

												# verify area
												lowerCAmelCase__        =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  _UpperCamelCase				)				)
												# verify boxes
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([17, 17, 63, 75, 75, 93]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify masks
												lowerCAmelCase__        =  82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  _UpperCamelCase				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)





