




"""simple docstring"""





import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A				=							logging.getLogger(__name__)



class   a__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Union[str, Any]   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Any=None):


						"""simple docstring"""




						super().__init__(
						    UpperCamelCase_   ,	question_encoder_tokenizer=UpperCamelCase_   ,	generator_tokenizer=UpperCamelCase_   ,	index=UpperCamelCase_   ,	init_retrieval=UpperCamelCase_   ,	)
						__UpperCAmelCase						:			Dict												=       None


			def    a_  (   self   :     Optional[int]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						logger.info("initializing retrieval")

						# initializing a separate process group for retrieval as the default
						# nccl backend doesn't support gather/scatter operations while gloo
						# is too slow to replace nccl for the core gpu communication
						if dist.is_initialized():
									logger.info("dist initialized")
									# needs to be set manually
									__UpperCAmelCase						:			Optional[Any]												=       self._infer_socket_ifname()
									# avoid clash with the NCCL port
									__UpperCAmelCase						:			Any												=       str(distributed_port + 1)
									__UpperCAmelCase						:			Optional[Any]												=       dist.new_group(ranks=UpperCamelCase_   ,	backend="gloo")

						# initialize retriever only on the main worker
						if not dist.is_initialized() or self._is_main():
									logger.info("dist not initialized / main")
									self.index.init_index()

						# all processes wait untill the retriever is initialized by the main process
						if dist.is_initialized():
									torch.distributed.barrier(group=self.process_group)


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						return dist.get_rank(group=self.process_group) == 0


			def    a_  (   self   :     List[str]   ,	UpperCamelCase_   :     Union[str, Any]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Dict=torch.floataa):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       torch.empty(UpperCamelCase_   ,	dtype=UpperCamelCase_)
						dist.scatter(UpperCamelCase_   ,	src=0   ,	scatter_list=UpperCamelCase_   ,	group=self.process_group)
						return target_tensor


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       psutil.net_if_addrs()
						# a hacky way to deal with varying network interface names
						__UpperCAmelCase						:			Dict												=       next((addr for addr in addrs if addr.startswith("e"))   ,	UpperCamelCase_)
						return ifname


			def    a_  (   self   :     Union[str, Any]   ,	UpperCamelCase_   :     np.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						if not dist.is_initialized():
									__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       self._main_retrieve(UpperCamelCase_   ,	UpperCamelCase_)
									return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_)

						# distributed training
						__UpperCAmelCase						:			Union[str, Any]												=       dist.get_world_size(group=self.process_group)

						# gather logic
						__UpperCAmelCase						:			List[Any]												=       None
						if self._is_main():
									__UpperCAmelCase						:			str												=       [torch.empty(question_hidden_states.shape   ,	dtype=torch.floataa) for _ in range(UpperCamelCase_)]
						dist.gather(torch.tensor(UpperCamelCase_)   ,	dst=0   ,	gather_list=UpperCamelCase_   ,	group=self.process_group)

						# scatter logic
						__UpperCAmelCase						:			Tuple												=       question_hidden_states.shape[0]
						__UpperCAmelCase						:			List[Any]												=       []
						__UpperCAmelCase						:			Union[str, Any]												=       []
						if self._is_main():
									assert len(UpperCamelCase_) == world_size
									__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       self._main_retrieve(torch.cat(UpperCamelCase_).numpy()   ,	UpperCamelCase_)
									__UpperCAmelCase						,  __UpperCAmelCase						:			int												=       torch.tensor(UpperCamelCase_), torch.tensor(UpperCamelCase_)
									__UpperCAmelCase						:			Optional[Any]												=       self._chunk_tensor(UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			int												=       self._chunk_tensor(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       self._scattered(UpperCamelCase_   ,	[n_queries, n_docs]   ,	target_type=torch.intaa)
						__UpperCAmelCase						:			Optional[int]												=       self._scattered(UpperCamelCase_   ,	[n_queries, n_docs, question_hidden_states.shape[1]])

						return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(UpperCamelCase_)






'''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 DeformableDetrImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=[0.5, 0.5, 0.5]			,     _UpperCamelCase=True			,     _UpperCamelCase=1 / 255			,     _UpperCamelCase=True			,     )->      Tuple:
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            _A				=							size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
            _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   UpperCamelCase						( self   )->      Union[str, Any]:
            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   )->      List[str]:
            if not batched:
                 _A				=							image_inputs[0]
                 if isinstance(_UpperCamelCase			,     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(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[0]   )[0]
                 _A				=							max(_UpperCamelCase			,     key=lambda _UpperCamelCase   : item[1]   )[1]

            return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DeformableDetrImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							DeformableDetrImageProcessingTester(self   )






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






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							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   )->      Any:
            _A				=							self.image_processing_class.from_dict(self.image_processor_dict   )
            self.assertEqual(image_processor.size			,     {'shortest_edge': 18, 'longest_edge': 1333}   )
            self.assertEqual(image_processor.do_pad			,     _UpperCamelCase   )

            _A				=							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   )->      Optional[Any]:
            pass






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processing
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     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(_UpperCamelCase   )

            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(_UpperCamelCase			,     batched=_UpperCamelCase   )

            _A				=							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   )->      List[str]:
            # Initialize image_processing
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							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
            _A				=							image_processing(image_inputs[0]			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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   )->      Dict:
            # Initialize image_processing
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							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
            _A				=							image_processing(image_inputs[0]			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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
            _A				=							image_processing(_UpperCamelCase			,     return_tensors='pt'   ).pixel_values

            _A		,				_A				=							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   )->      List[Any]:
            # prepare image and target
            _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': 3_9769, 'annotations': target}

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

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

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

            # verify area
            _A				=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )






       @slow
       def   UpperCamelCase						( self   )->      Any:
            # prepare image, target and masks_path
            _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': 3_9769, 'segments_info': target}

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

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

            # verify pixel values
            _A				=							torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape			,     _UpperCamelCase   )

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

            # verify area
            _A				=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,     _UpperCamelCase   )   )
            # verify boxes
            _A				=							torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape			,     _UpperCamelCase   )
            _A				=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,     _UpperCamelCase			,     atol=1e-3   )   )
            # verify image_id
            _A				=							torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,     _UpperCamelCase   )   )
            # verify is_crowd
            _A				=							torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,     _UpperCamelCase   )   )
            # verify class_labels
            _A				=							torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,     _UpperCamelCase   )   )
            # verify masks
            _A				=							82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,     _UpperCamelCase   )
            # verify orig_size
            _A				=							torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,     _UpperCamelCase   )   )
            # verify size
            _A				=							torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,     _UpperCamelCase   )   )





