

'''simple docstring'''

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 ViltImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				def __init__(       self,						lowercase_,						lowercase_=7,						lowercase_=3,						lowercase_=18,						lowercase_=30,						lowercase_=400,						lowercase_=True,						lowercase_=None,						lowercase_=2,						lowercase_=True,						lowercase_=[0.5, 0.5, 0.5],						lowercase_=[0.5, 0.5, 0.5],						)    ->							Optional[Any]:
					snake_case							=					size if size is not None else {'shortest_edge': 30}
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					num_channels
					snake_case							=					image_size
					snake_case							=					min_resolution
					snake_case							=					max_resolution
					snake_case							=					do_resize
					snake_case							=					size
					snake_case							=					size_divisor
					snake_case							=					do_normalize
					snake_case							=					image_mean
					snake_case							=					image_std

				def 					_lowerCamelCase    (       self )    ->							Any:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_=False )    ->							Optional[int]:
					if not batched:
						snake_case							=					self.size['shortest_edge']
						snake_case							=					image_inputs[0]
						if isinstance(lowercase_,						Image.Image ):
							snake_case			,			snake_case							=					image.size
						else:
							snake_case			,			snake_case							=					image.shape[1], image.shape[2]
						snake_case							=					size / min(lowercase_,						lowercase_ )
						if h < w:
							snake_case			,			snake_case							=					size, scale * w
						else:
							snake_case			,			snake_case							=					scale * h, size

						snake_case							=					int((1333 / 800) * size )
						if max(lowercase_,						lowercase_ ) > max_size:
							snake_case							=					max_size / max(lowercase_,						lowercase_ )
							snake_case							=					newh * scale
							snake_case							=					neww * scale

						snake_case			,			snake_case							=					int(newh + 0.5 ), int(neww + 0.5 )
						snake_case			,			snake_case							=					(
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

					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(lowercase_,						key=lambda lowercase_ : item[0] )[0]
						snake_case							=					max(lowercase_,						key=lambda lowercase_ : item[1] )[1]

					return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase   (    __lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = ViltImageProcessor if is_vision_available() else None

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					ViltImageProcessingTester(self )

				@property
				def 					_lowerCamelCase    (       self )    ->							int:
					return self.image_processor_tester.prepare_image_processor_dict()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(lowercase_,						'image_mean' ) )
					self.assertTrue(hasattr(lowercase_,						'image_std' ) )
					self.assertTrue(hasattr(lowercase_,						'do_normalize' ) )
					self.assertTrue(hasattr(lowercase_,						'do_resize' ) )
					self.assertTrue(hasattr(lowercase_,						'size' ) )
					self.assertTrue(hasattr(lowercase_,						'size_divisor' ) )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size,						{'shortest_edge': 30} )

					snake_case							=					self.image_processing_class.from_dict(self.image_processor_dict,						size=42 )
					self.assertEqual(image_processor.size,						{'shortest_edge': 42} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					# 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=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						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(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# 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=lowercase_,						numpify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						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(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					# 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=lowercase_,						torchify=lowercase_ )
					for image in image_inputs:
						self.assertIsInstance(lowercase_,						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(lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(1, self.image_processor_tester.num_channels, expected_height, expected_width),						)

					# Test batched
					snake_case							=					image_processing(lowercase_,						return_tensors='pt' ).pixel_values

					snake_case			,			snake_case							=					self.image_processor_tester.get_expected_values(lowercase_,						batched=lowercase_ )
					self.assertEqual(
					    encoded_images.shape,						(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),						)







"""simple docstring"""

import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


_A				=					'block_records.npy'


_A				=					logging.get_logger(__name__)
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		import tensorflow.compat.va as tf

		lowerCamelCase	:     Union[str, Any]            =       tf.data.TFRecordDataset(a_,		buffer_size=512 * 1024 * 1024     )
		lowerCamelCase	:     Union[str, Any]            =       blocks_dataset.batch(a_,		drop_remainder=a_     )
		lowerCamelCase	:     Union[str, Any]            =       next(blocks_dataset.take(1     ).as_numpy_iterator()     )

		return np_record






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=2						,					UpperCAmelCase_=1000						,					UpperCAmelCase_=100						,					UpperCAmelCase_=100000						,					)						->				List[Any]:
			from scann.scann_ops.py.scann_ops_pybind import builder as Builder

			lowerCamelCase	:     int            =       Builder(db=UpperCAmelCase_						,					num_neighbors=UpperCAmelCase_						,					distance_measure='dot_product'   )
			lowerCamelCase	:     Optional[int]            =       builder.tree(
			    num_leaves=UpperCAmelCase_						,					num_leaves_to_search=UpperCAmelCase_						,					training_sample_size=UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       builder.score_ah(dimensions_per_block=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       builder.build()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       self.searcher.search_batched(question_projection.detach().cpu()   )
			return retrieved_block_ids.astype('int64'   )






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			super().__init__()
			lowerCamelCase	:     Tuple            =       block_records
			lowerCamelCase	:     Any            =       tokenizer




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_="pt"   )						->				Tuple:
			lowerCamelCase	:     List[str]            =       np.take(self.block_records						,					indices=UpperCAmelCase_						,					axis=0   )

			lowerCamelCase	:     Any            =       self.tokenizer.decode(question_input_ids[0]						,					skip_special_tokens=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Optional[int]            =       []
			for retrieved_block in retrieved_blocks:
					text.append(UpperCAmelCase_   )
					text_pair.append(retrieved_block.decode()   )

			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					padding=UpperCAmelCase_						,					truncation=UpperCAmelCase_						,					return_special_tokens_mask=UpperCAmelCase_						,					max_length=UpperCAmelCase_   )
			lowerCamelCase	:     str            =       concat_inputs.convert_to_tensors(UpperCAmelCase_   )

			if answer_ids is not None:
					return self.block_has_answer(UpperCAmelCase_						,					UpperCAmelCase_   ) + (concat_inputs_tensors,)
			else:
					return (None, None, None, concat_inputs_tensors)




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Dict:
			if os.path.isdir(UpperCAmelCase_   ):
					lowerCamelCase	:     str            =       os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )
			else:
					lowerCamelCase	:     Tuple            =       hf_hub_download(
					    repo_id=UpperCAmelCase_						,					filename=_REALM_BLOCK_RECORDS_FILENAME						,					**UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       np.load(UpperCAmelCase_						,					allow_pickle=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       AutoTokenizer.from_pretrained(UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )

			return cls(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			# save block records
			np.save(os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )						,					self.block_records   )
			# save tokenizer
			self.tokenizer.save_pretrained(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     List[Any]            =       []
			lowerCamelCase	:     Any            =       []
			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Union[str, Any]            =       0

			for input_id in concat_inputs.input_ids:
					lowerCamelCase	:     Optional[Any]            =       input_id.tolist()
					# Check answers between two [SEP] tokens
					lowerCamelCase	:     Tuple            =       input_id_list.index(self.tokenizer.sep_token_id   )
					lowerCamelCase	:     Any            =       first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id   )

					start_pos.append([]   )
					end_pos.append([]   )
					for answer in answer_ids:
							for idx in range(first_sep_idx + 1						,					UpperCAmelCase_   ):
									if answer[0] == input_id_list[idx]:
											if input_id_list[idx : idx + len(UpperCAmelCase_   )] == answer:
													start_pos[-1].append(UpperCAmelCase_   )
													end_pos[-1].append(idx + len(UpperCAmelCase_   ) - 1   )

					if len(start_pos[-1]   ) == 0:
							has_answers.append(UpperCAmelCase_   )
					else:
							has_answers.append(UpperCAmelCase_   )
							if len(start_pos[-1]   ) > max_answers:
									lowerCamelCase	:     List[str]            =       len(start_pos[-1]   )

        # Pad -1 to max_answers
			for start_pos_, end_pos_ in zip(UpperCAmelCase_						,					UpperCAmelCase_   ):
					if len(start_pos_   ) < max_answers:
							lowerCamelCase	:     Optional[int]            =       [-1] * (max_answers - len(start_pos_   ))
							start_pos_ += padded
							end_pos_ += padded
			return has_answers, start_pos, end_pos






