





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			):



     '''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						,   ):
       _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	):
       _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=_lowercase						,   initializer_range=self.initializer_range						,   )

       return config, input_ids, token_type_ids, attention_mask






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       _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       __SCREAMING_SNAKE_CASE	(						SCREAMING_SNAKE_CASE__					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   True

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				FlaxRoFormerModelTester(self	)






     @slow
     def 	UpperCamelCase(					self	):

       for model_class_name in self.all_model_classes:
         _snake_case     		=				model_class_name.from_pretrained("junnyu/roformer_chinese_small"						,   from_pt=_lowercase	)
         _snake_case     		=				model(np.ones((1, 1)	)	)
         self.assertIsNotNone(_lowercase	)







@require_flax
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
       _snake_case     		=				jnp.array([[0, 1, 2, 3, 4, 5]]	)
       _snake_case     		=				model(_lowercase	)[0]

       _snake_case     		=				50_000

       _snake_case     		=				(1, 6, vocab_size)
       self.assertEqual(output.shape						,   _lowercase	)

       _snake_case     		=				jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]	)

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









'''simple docstring'''



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 DetaImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=True						,   lowerCamelCase=1 / 255						,   lowerCamelCase=True						,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case     		=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_pad

     def 	UpperCamelCase(					self	):
       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						,   lowerCamelCase						,   lowerCamelCase=False	):
       if not batched:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   Image.Image	):
           _snake_case		,		_snake_case     		=				image.size
         else:
           _snake_case		,		_snake_case     		=				image.shape[1], image.shape[2]
         if w < h:
           _snake_case     		=				int(self.size["shortest_edge"] * h / w	)
           _snake_case     		=				self.size["shortest_edge"]
         elif w > h:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				int(self.size["shortest_edge"] * w / h	)
         else:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				self.size["shortest_edge"]

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

       return expected_height, expected_width







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   DetaImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				DetaImageProcessingTester(self	)

     @property
     def 	UpperCamelCase(					self	):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_mean"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_std"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_normalize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_resize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_rescale"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_pad"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size						,   {"shortest_edge": 18, "longest_edge": 1_333}	)
       self.assertEqual(image_processor.do_pad						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # 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=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processing(lowerCamelCase						,   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	):
       # 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=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   np.ndarray	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

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

     def 	UpperCamelCase(					self	):
       # 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=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   torch.Tensor	)

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

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

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

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

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

       _snake_case     		=				{"image_id": 39_769, "annotations": target}

       # encode them
       _snake_case     		=				DetaImageProcessor()
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

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

       # verify area
       _snake_case     		=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)






     @slow
     def 	UpperCamelCase(					self	):
       # prepare image, target and masks_path
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

       _snake_case     		=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

       # encode them
       _snake_case     		=				DetaImageProcessor(format="coco_panoptic"	)
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   masks_path=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

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

       # verify area
       _snake_case     		=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify masks
       _snake_case     		=				822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,   lowerCamelCase	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)


