import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 OwlViTImageProcessor




class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=7				,       lowerCAmelCase__=3				,       lowerCAmelCase__=18				,       lowerCAmelCase__=30				,       lowerCAmelCase__=4_00				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=[0.4814_5466, 0.457_8275, 0.4082_1073]				,       lowerCAmelCase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,       lowerCAmelCase__=True				,       )					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							num_channels
							__lowercase			      =							image_size
							__lowercase			      =							min_resolution
							__lowercase			      =							max_resolution
							__lowercase			      =							do_resize
							__lowercase			      =							size if size is not None else {"""height""": 18, """width""": 18}
							__lowercase			      =							do_center_crop
							__lowercase			      =							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							__lowercase			      =							do_normalize
							__lowercase			      =							image_mean
							__lowercase			      =							image_std
							__lowercase			      =							do_convert_rgb





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

							'''simple docstring'''


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}




@require_torch
@require_vision
class        _UpperCamelCase				(						_a						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			str        =						OwlViTImageProcessor if is_vision_available() else None





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

							'''simple docstring'''


							__lowercase			      =							OwlViTImageProcessingTester(self						)





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





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

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(snake_case_				,       '''do_resize'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''size'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''do_center_crop'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''center_crop'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''do_normalize'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''image_mean'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''image_std'''						)						)
							self.assertTrue(hasattr(snake_case_				,       '''do_convert_rgb'''						)						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size				,       {'''height''': 18, '''width''': 18}						)
							self.assertEqual(image_processor.crop_size				,       {'''height''': 18, '''width''': 18}						)

							__lowercase			      =							self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84						)
							self.assertEqual(image_processor.size				,       {'''height''': 42, '''width''': 42}						)
							self.assertEqual(image_processor.crop_size				,       {'''height''': 84, '''width''': 84}						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__lowercase			      =							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
							__lowercase			      =							image_processing(image_inputs[0]				,       return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape				,       (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )

							# Test batched
							__lowercase			      =							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,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__lowercase			      =							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
							__lowercase			      =							image_processing(image_inputs[0]				,       return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape				,       (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )

							# Test batched
							__lowercase			      =							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,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__lowercase			      =							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
							__lowercase			      =							image_processing(image_inputs[0]				,       return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape				,       (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )

							# Test batched
							__lowercase			      =							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,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )				,       )
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__a  :					Optional[Any]          =  logging.getLogger(__name__)




class        _UpperCamelCase				:


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							if not self.initialized:
											__lowercase			      =							RagRetriever(
											    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )
											__lowercase			      =							True





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							self.retriever.index.init_index()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							del self.retriever
							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.retriever._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.retriever.index.get_doc_dicts(lowerCAmelCase__						)
							return doc_ids, retrieved_doc_embeds, doc_dicts




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if index is not None and index.is_initialized() and len(lowerCAmelCase__						) > 0:
											raise ValueError(
											    '''When using Ray for distributed fine-tuning, '''
											    '''you\'ll need to provide the paths instead, '''
											    '''as the dataset and the index are loaded '''
											    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''						)

							super().__init__(
							    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )

							__lowercase			      =							retrieval_workers
							__lowercase			      =							question_encoder_tokenizer
							__lowercase			      =							generator_tokenizer
							if len(self.retrieval_workers						) > 0:
											ray.get(
											    [
											        worker.create_rag_retriever.remote(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
											        for worker in self.retrieval_workers
											    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							logger.info('''initializing retrieval'''						)

							if len(self.retrieval_workers						) > 0:
											ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
							else:
											# Non-distributed training. Load index into this same process.
											self.index.init_index()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							if len(self.retrieval_workers						) > 0:
											# Select a random retrieval actor.
											__lowercase			      =							self.retrieval_workers[random.randint(0				,       len(self.retrieval_workers						) - 1						)]
											__lowercase      ,					__lowercase      ,					__lowercase			      =							ray.get(
											    random_worker.retrieve.remote(lowerCAmelCase__				,       lowerCAmelCase__						)						)
							else:
											__lowercase      ,					__lowercase			      =							self._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							self.index.get_doc_dicts(lowerCAmelCase__						)
							return retrieved_doc_embeds, doc_ids, doc_dicts





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							return super(lowerCAmelCase__				,       cls						).get_tokenizers(lowerCAmelCase__				,       lowerCAmelCase__				,       **lowerCAmelCase__						)





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						) or RagConfig.from_pretrained(lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							RagTokenizer.from_pretrained(lowerCAmelCase__				,       config=lowerCAmelCase__						)
							__lowercase			      =							rag_tokenizer.question_encoder
							__lowercase			      =							rag_tokenizer.generator

							if indexed_dataset is not None:
											__lowercase			      =							'''custom'''
											__lowercase			      =							CustomHFIndex(config.retrieval_vector_size				,       lowerCAmelCase__						)
							else:
											__lowercase			      =							cls._build_index(lowerCAmelCase__						)

							return cls(
							    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       retrieval_workers=lowerCAmelCase__				,       index=lowerCAmelCase__				,       )





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							logger.info('''re-loading the new dataset with embeddings'''						)
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]						)

							# build the index object again
							__lowercase			      =							self._build_index(self.config						)

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,       self.question_encoder_tokenizer				,       self.generator_tokenizer				,       lowerCAmelCase__						)
							        for worker in self.retrieval_workers
							    ]						)