



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    _A	(     unittest.TestCase   ):



       def __init__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[str]=7					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=3					,		__SCREAMING_SNAKE_CASE							:					str=30					,		__SCREAMING_SNAKE_CASE							:					int=400					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=True					,		__SCREAMING_SNAKE_CASE							:					int=None					,		__SCREAMING_SNAKE_CASE							:					int=True					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=[0.5, 0.5, 0.5]					,		__SCREAMING_SNAKE_CASE							:					Any=[0.5, 0.5, 0.5]					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					Tuple=1 / 255					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		):


           '''simple docstring'''

           __a							=    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           __a							=    parent
           __a							=    batch_size
           __a							=    num_channels
           __a							=    min_resolution
           __a							=    max_resolution
           __a							=    do_resize
           __a							=    size
           __a							=    do_normalize
           __a							=    image_mean
           __a							=    image_std
           __a							=    do_rescale
           __a							=    rescale_factor
           __a							=    do_pad



       def 		_lowerCamelCase      (		self							:					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							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=False):


           '''simple docstring'''

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

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

           return expected_height, expected_width


@require_torch
@require_vision
class    _A	(     _a				,unittest.TestCase   ):
       UpperCamelCase__  :   Union[str, Any]						 =						DeformableDetrImageProcessor if is_vision_available() else None



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    DeformableDetrImageProcessingTester(self)



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    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 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    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)

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



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           __a							=    prepare_image_inputs(self.image_processor_tester					,		equal_resolution=__lowerCAmelCase)
           for image in image_inputs:
               self.assertIsInstance(__lowerCAmelCase					,		Image.Image)

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

           __a	,       __a							=    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
           __a	,       __a							=    self.image_processor_tester.get_expected_values(__lowerCAmelCase					,		batched=__lowerCAmelCase)

           __a							=    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 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a							=    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
           __a							=    image_processing(image_inputs[0]					,		return_tensors='''pt''').pixel_values

           __a	,       __a							=    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
           __a							=    image_processing(__lowerCAmelCase					,		return_tensors='''pt''').pixel_values

           __a	,       __a							=    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 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a							=    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
           __a							=    image_processing(image_inputs[0]					,		return_tensors='''pt''').pixel_values

           __a	,       __a							=    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
           __a							=    image_processing(__lowerCAmelCase					,		return_tensors='''pt''').pixel_values

           __a	,       __a							=    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 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''					,		'''r''') as f:
               __a							=    json.loads(f.read())

           __a							=    {'''image_id''': 39_769, '''annotations''': target}

           # encode them
           __a							=    DeformableDetrImageProcessor()
           __a							=    image_processing(images=__lowerCAmelCase					,		annotations=__lowerCAmelCase					,		return_tensors='''pt''')

           # verify pixel values
           __a							=    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					,		__lowerCAmelCase)

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

           # verify area
           __a							=    torch.tensor([58_87.96_00, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					,		__lowerCAmelCase))
           # verify boxes
           __a							=    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,		__lowerCAmelCase)
           __a							=    torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,		__lowerCAmelCase					,		atol=1E-3))
           # verify image_id
           __a							=    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,		__lowerCAmelCase))
           # verify is_crowd
           __a							=    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,		__lowerCAmelCase))
           # verify class_labels
           __a							=    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,		__lowerCAmelCase))
           # verify orig_size
           __a							=    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,		__lowerCAmelCase))
           # verify size
           __a							=    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,		__lowerCAmelCase))





       @slow
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''					,		'''r''') as f:
               __a							=    json.loads(f.read())

           __a							=    {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

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

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

           # verify pixel values
           __a							=    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					,		__lowerCAmelCase)

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

           # verify area
           __a							=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 58_79.65_62, 76_34.11_47])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					,		__lowerCAmelCase))
           # verify boxes
           __a							=    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					,		__lowerCAmelCase)
           __a							=    torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					,		__lowerCAmelCase					,		atol=1E-3))
           # verify image_id
           __a							=    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					,		__lowerCAmelCase))
           # verify is_crowd
           __a							=    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					,		__lowerCAmelCase))
           # verify class_labels
           __a							=    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					,		__lowerCAmelCase))
           # verify masks
           __a							=    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					,		__lowerCAmelCase)
           # verify orig_size
           __a							=    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					,		__lowerCAmelCase))
           # verify size
           __a							=    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					,		__lowerCAmelCase))










import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case						:int						=					logging.get_logger(__name__)

class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    question_encoder
           __a							=    generator
           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if os.path.isfile(__SCREAMING_SNAKE_CASE):
               raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
           os.makedirs(__SCREAMING_SNAKE_CASE					,		exist_ok=__SCREAMING_SNAKE_CASE)
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''question_encoder_tokenizer''')
           __a							=    os.path.join(__SCREAMING_SNAKE_CASE					,		'''generator_tokenizer''')
           self.question_encoder.save_pretrained(__SCREAMING_SNAKE_CASE)
           self.generator.save_pretrained(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase      (		cls							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           from ..auto.tokenization_auto import AutoTokenizer

           __a							=    kwargs.pop('''config'''					,		__SCREAMING_SNAKE_CASE)

           if config is None:
               __a							=    RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.question_encoder					,		subfolder='''question_encoder_tokenizer''')
           __a							=    AutoTokenizer.from_pretrained(
               __SCREAMING_SNAKE_CASE					,		config=config.generator					,		subfolder='''generator_tokenizer''')
           return cls(question_encoder=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE)



       def __call__(		self							:					Dict					,		*__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.current_tokenizer(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		*__SCREAMING_SNAKE_CASE							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return self.generator.batch_decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           return self.generator.decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.question_encoder



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.generator





       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					str = "longest"					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           warnings.warn(
               '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
               '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
               '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
               '''details'''					,		__SCREAMING_SNAKE_CASE					,		)
           if max_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               __SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           if tgt_texts is None:
               return model_inputs
           # Process tgt_texts
           if max_target_length is None:
               __a							=    self.current_tokenizer.model_max_length
           __a							=    self(
               text_target=__SCREAMING_SNAKE_CASE					,		add_special_tokens=__SCREAMING_SNAKE_CASE					,		return_tensors=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		max_length=__SCREAMING_SNAKE_CASE					,		truncation=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    labels['''input_ids''']
           return model_inputs





