


'''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 ConditionalDetrImageProcessor






class  __magic_name__							(		unittest.TestCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case=7  ,							snake_case=3  ,							snake_case=3_0  ,							snake_case=4_0_0  ,							snake_case=True  ,							snake_case=None  ,							snake_case=True  ,							snake_case=[0.5, 0.5, 0.5]  ,							snake_case=[0.5, 0.5, 0.5]  ,							snake_case=True  ,							snake_case=1 / 2_5_5  ,							snake_case=True  ,							)						->  Tuple:




          '''simple docstring'''
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          _UpperCAmelCase       :       Optional[int]							   =size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
          _UpperCAmelCase       :       List[Any]							   =parent
          _UpperCAmelCase       :       Union[str, Any]							   =batch_size
          _UpperCAmelCase       :       List[Any]							   =num_channels
          _UpperCAmelCase       :       List[str]							   =min_resolution
          _UpperCAmelCase       :       Optional[int]							   =max_resolution
          _UpperCAmelCase       :       Any							   =do_resize
          _UpperCAmelCase       :       List[Any]							   =size
          _UpperCAmelCase       :       str							   =do_normalize
          _UpperCAmelCase       :       Dict							   =image_mean
          _UpperCAmelCase       :       Tuple							   =image_std
          _UpperCAmelCase       :       Optional[Any]							   =do_rescale
          _UpperCAmelCase       :       List[str]							   =rescale_factor
          _UpperCAmelCase       :       int							   =do_pad






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




          '''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       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=False)						->  Any:




          '''simple docstring'''
          if not batched:
             _UpperCAmelCase       :       List[Any]							   =image_inputs[0]
             if isinstance(snake_case  ,							Image.Image):
                _UpperCAmelCase   ,	_UpperCAmelCase       :       int							   =image.size
             else:
                _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =image.shape[1], image.shape[2]
             if w < h:
                _UpperCAmelCase       :       List[str]							   =int(self.size['shortest_edge'] * h / w)
                _UpperCAmelCase       :       List[Any]							   =self.size['shortest_edge']
             elif w > h:
                _UpperCAmelCase       :       List[Any]							   =self.size['shortest_edge']
                _UpperCAmelCase       :       Optional[Any]							   =int(self.size['shortest_edge'] * w / h)
             else:
                _UpperCAmelCase       :       int							   =self.size['shortest_edge']
                _UpperCAmelCase       :       str							   =self.size['shortest_edge']

          else:
             _UpperCAmelCase       :       List[Any]							   =[]
             for image in image_inputs:
                _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
             _UpperCAmelCase       :       List[str]							   =max(snake_case  ,							key=lambda snake_case: item[0])[0]
             _UpperCAmelCase       :       List[str]							   =max(snake_case  ,							key=lambda snake_case: item[1])[1]

          return expected_height, expected_width






@require_torch
@require_vision
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=ConditionalDetrImageProcessor if is_vision_available() else None






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =ConditionalDetrImageProcessingTester(self)






       @property
       def       lowerCAmelCase		(      self)						->  Any:




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






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case  ,							'image_mean'))
          self.assertTrue(hasattr(snake_case  ,							'image_std'))
          self.assertTrue(hasattr(snake_case  ,							'do_normalize'))
          self.assertTrue(hasattr(snake_case  ,							'do_resize'))
          self.assertTrue(hasattr(snake_case  ,							'size'))






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size  ,							{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3})
          self.assertEqual(image_processor.do_pad  ,							snake_case)

          _UpperCAmelCase       :       Any							   =self.image_processing_class.from_dict(
              self.image_processor_dict  ,							size=4_2  ,							max_size=8_4  ,							pad_and_return_pixel_mask=snake_case)
          self.assertEqual(image_processor.size  ,							{'shortest_edge': 4_2, 'longest_edge': 8_4})
          self.assertEqual(image_processor.do_pad  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       int							   =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _UpperCAmelCase       :       Optional[Any]							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							Image.Image)

          # Test not batched input
          _UpperCAmelCase       :       Union[str, Any]							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values

          _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =self.image_processor_tester.get_expected_values(snake_case)

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

          # Test batched
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =self.image_processor_tester.get_expected_values(snake_case  ,							batched=snake_case)

          _UpperCAmelCase       :       str							   =image_processing(snake_case  ,							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       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       str							   =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _UpperCAmelCase       :       Tuple							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case  ,							numpify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							np.ndarray)

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

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =self.image_processor_tester.get_expected_values(snake_case)

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

          # Test batched
          _UpperCAmelCase       :       Any							   =image_processing(snake_case  ,							return_tensors='pt').pixel_values

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =self.image_processor_tester.get_expected_values(snake_case  ,							batched=snake_case)

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






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          # Initialize image_processing
          _UpperCAmelCase       :       str							   =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _UpperCAmelCase       :       List[Any]							   =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=snake_case  ,							torchify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case  ,							torch.Tensor)

          # Test not batched input
          _UpperCAmelCase       :       Optional[Any]							   =image_processing(image_inputs[0]  ,							return_tensors='pt').pixel_values

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Tuple							   =self.image_processor_tester.get_expected_values(snake_case)

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

          # Test batched
          _UpperCAmelCase       :       Dict							   =image_processing(snake_case  ,							return_tensors='pt').pixel_values

          _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =self.image_processor_tester.get_expected_values(snake_case  ,							batched=snake_case)

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






       @slow
       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          # prepare image and target
          _UpperCAmelCase       :       Tuple							   =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
          with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'  ,							'r') as f:
             _UpperCAmelCase       :       Union[str, Any]							   =json.loads(f.read())

          _UpperCAmelCase       :       List[str]							   ={'image_id': 3_9_7_6_9, 'annotations': target}

          # encode them
          _UpperCAmelCase       :       List[str]							   =ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50')
          _UpperCAmelCase       :       str							   =image_processing(images=snake_case  ,							annotations=snake_case  ,							return_tensors='pt')

          # verify pixel values
          _UpperCAmelCase       :       Tuple							   =torch.Size([1, 3, 8_0_0, 1_0_6_6])
          self.assertEqual(encoding['pixel_values'].shape  ,							snake_case)

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

          # verify area
          _UpperCAmelCase       :       List[Any]							   =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']  ,							snake_case))
          # verify boxes
          _UpperCAmelCase       :       int							   =torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape  ,							snake_case)
          _UpperCAmelCase       :       int							   =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,							snake_case  ,							atol=1E-3))
          # verify image_id
          _UpperCAmelCase       :       List[str]							   =torch.tensor([3_9_7_6_9])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,							snake_case))
          # verify is_crowd
          _UpperCAmelCase       :       str							   =torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,							snake_case))
          # verify class_labels
          _UpperCAmelCase       :       List[str]							   =torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,							snake_case))
          # verify orig_size
          _UpperCAmelCase       :       Any							   =torch.tensor([4_8_0, 6_4_0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,							snake_case))
          # verify size
          _UpperCAmelCase       :       List[str]							   =torch.tensor([8_0_0, 1_0_6_6])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,							snake_case))






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




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

          _UpperCAmelCase       :       Tuple							   ={'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

          _UpperCAmelCase       :       Any							   =pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

          # encode them
          _UpperCAmelCase       :       Optional[Any]							   =ConditionalDetrImageProcessor(format='coco_panoptic')
          _UpperCAmelCase       :       int							   =image_processing(images=snake_case  ,							annotations=snake_case  ,							masks_path=snake_case  ,							return_tensors='pt')

          # verify pixel values
          _UpperCAmelCase       :       List[Any]							   =torch.Size([1, 3, 8_0_0, 1_0_6_6])
          self.assertEqual(encoding['pixel_values'].shape  ,							snake_case)

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

          # verify area
          _UpperCAmelCase       :       Any							   =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']  ,							snake_case))
          # verify boxes
          _UpperCAmelCase       :       Optional[int]							   =torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape  ,							snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]  ,							snake_case  ,							atol=1E-3))
          # verify image_id
          _UpperCAmelCase       :       Tuple							   =torch.tensor([3_9_7_6_9])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,							snake_case))
          # verify is_crowd
          _UpperCAmelCase       :       str							   =torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,							snake_case))
          # verify class_labels
          _UpperCAmelCase       :       Tuple							   =torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']  ,							snake_case))
          # verify masks
          _UpperCAmelCase       :       Dict							   =8_2_2_8_7_3
          self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,							snake_case)
          # verify orig_size
          _UpperCAmelCase       :       str							   =torch.tensor([4_8_0, 6_4_0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,							snake_case))
          # verify size
          _UpperCAmelCase       :       int							   =torch.tensor([8_0_0, 1_0_6_6])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,							snake_case))





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

