

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[int]         =		tempfile.mkdtemp()
												_a	:    Union[str, Any]         =		5

												# Realm tok
												_a	:    Tuple         =		[
												    '''[UNK]''',
												    '''[CLS]''',
												    '''[SEP]''',
												    '''[PAD]''',
												    '''[MASK]''',
												    '''test''',
												    '''question''',
												    '''this''',
												    '''is''',
												    '''the''',
												    '''first''',
												    '''second''',
												    '''third''',
												    '''fourth''',
												    '''fifth''',
												    '''record''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												_a	:    Dict         =		os.path.join(self.tmpdirname       ,							'''realm_tokenizer'''       )
												os.makedirs(_a       ,							exist_ok=_a       )
												_a	:    Tuple         =		os.path.join(_a       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
												with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as vocab_writer:
																	vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

												_a	:    Tuple         =		os.path.join(self.tmpdirname       ,							'''realm_block_records'''       )
												os.makedirs(_a       ,							exist_ok=_a       )


							def 				__lowercase				(			self       )    ->    RealmTokenizer:
												return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,							'''realm_tokenizer'''       )       )


							def 				__lowercase				(			self       )    ->    List[str]:
												shutil.rmtree(self.tmpdirname       )


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    str         =		RealmConfig(num_block_records=self.num_block_records       )
												return config


							def 				__lowercase				(			self       )    ->    str:
												_a	:    str         =		Dataset.from_dict(
												    {
												        '''id''': ['''0''', '''1'''],
												        '''question''': ['''foo''', '''bar'''],
												        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
												    }       )
												return dataset


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Optional[Any]         =		np.array(
												    [
												        b'''This is the first record''',
												        b'''This is the second record''',
												        b'''This is the third record''',
												        b'''This is the fourth record''',
												        b'''This is the fifth record''',
												        b'''This is a longer longer longer record''',
												    ]       ,							dtype=_a       ,							)
												return block_records


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    Optional[int]         =		RealmRetriever(
												    block_records=self.get_dummy_block_records()       ,							tokenizer=self.get_tokenizer()       ,							)
												return retriever


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Dict         =		self.get_config()
												_a	:    int         =		self.get_dummy_retriever()
												_a	:    str         =		retriever.tokenizer

												_a	:    List[str]         =		np.array([0, 3]       ,							dtype='''long'''       )
												_a	:    str         =		tokenizer(['''Test question''']       ).input_ids
												_a	:    Optional[int]         =		tokenizer(
												    ['''the fourth''']       ,							add_special_tokens=_a       ,							return_token_type_ids=_a       ,							return_attention_mask=_a       ,							).input_ids
												_a	:    Tuple         =		config.reader_seq_len

												_a      ,					_a      ,					_a      ,					_a	:    str         =		retriever(
												    _a       ,							_a       ,							answer_ids=_a       ,							max_length=_a       ,							return_tensors='''np'''       )

												self.assertEqual(len(_a       )       ,							2       )
												self.assertEqual(len(_a       )       ,							2       )
												self.assertEqual(len(_a       )       ,							2       )
												self.assertEqual(concat_inputs.input_ids.shape       ,							(2, 1_0)       )
												self.assertEqual(concat_inputs.attention_mask.shape       ,							(2, 1_0)       )
												self.assertEqual(concat_inputs.token_type_ids.shape       ,							(2, 1_0)       )
												self.assertEqual(concat_inputs.special_tokens_mask.shape       ,							(2, 1_0)       )
												self.assertEqual(
												    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]       )       ,							['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']       ,							)
												self.assertEqual(
												    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]       )       ,							['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']       ,							)


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		self.get_config()
												_a	:    Optional[Any]         =		self.get_dummy_retriever()
												_a	:    int         =		retriever.tokenizer

												_a	:    Any         =		np.array([0, 3, 5]       ,							dtype='''long'''       )
												_a	:    List[str]         =		tokenizer(['''Test question''']       ).input_ids
												_a	:    List[Any]         =		tokenizer(
												    ['''the fourth''', '''longer longer''']       ,							add_special_tokens=_a       ,							return_token_type_ids=_a       ,							return_attention_mask=_a       ,							).input_ids
												_a	:    Any         =		config.reader_seq_len

												_a      ,					_a      ,					_a      ,					_a	:    Dict         =		retriever(
												    _a       ,							_a       ,							answer_ids=_a       ,							max_length=_a       ,							return_tensors='''np'''       )

												self.assertEqual([False, True, True]       ,							_a       )
												self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]       ,							_a       )
												self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]       ,							_a       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    int         =		self.get_dummy_retriever()
												retriever.save_pretrained(os.path.join(self.tmpdirname       ,							'''realm_block_records'''       )       )

												# Test local path
												_a	:    Dict         =		retriever.from_pretrained(os.path.join(self.tmpdirname       ,							'''realm_block_records'''       )       )
												self.assertEqual(retriever.block_records[0]       ,							b'''This is the first record'''       )

												# Test mocked remote path
												with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''       ) as mock_hf_hub_download:
																	_a	:    Union[str, Any]         =		os.path.join(
																	    os.path.join(self.tmpdirname       ,							'''realm_block_records'''       )       ,							_REALM_BLOCK_RECORDS_FILENAME       )
																	_a	:    Tuple         =		RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''       )

												self.assertEqual(retriever.block_records[0]       ,							b'''This is the first record'''       )






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			np.ndarray		,__a      :			Union[int, Iterable[int]]		,__a      :			bool		,__a      :			int	)			->     Tuple[int, int]:

					"""simple docstring"""

					def constraint_to_multiple_of(__a      :			List[str]		,__a      :			Dict		,__a      :			Any=0		,__a      :			int=None	):
										_a	:    Dict         =		round(val / multiple	) * multiple

										if max_val is not None and x > max_val:
															_a	:    Any         =		math.floor(val / multiple	) * multiple

										if x < min_val:
															_a	:    Dict         =		math.ceil(val / multiple	) * multiple

										return x

					_a	:    Union[str, Any]         =		(output_size, output_size) if isinstance(__a		,__a	) else output_size

					_a      ,					_a	:    List[Any]         =		get_image_size(__a	)
					_a      ,					_a	:    Any         =		output_size

					# determine new height and width
					_a	:    Union[str, Any]         =		output_height / input_height
					_a	:    Tuple         =		output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width	) < abs(1 - scale_height	):
															# fit width
															_a	:    Optional[Any]         =		scale_width
										else:
															# fit height
															_a	:    Tuple         =		scale_height

					_a	:    Optional[Any]         =		constraint_to_multiple_of(scale_height * input_height		,multiple=__a	)
					_a	:    int         =		constraint_to_multiple_of(scale_width * input_width		,multiple=__a	)

					return (new_height, new_width)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = False       ,							_a = 1       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a	:    Optional[Any]         =		get_size_dict(_a       )
												_a	:    Any         =		do_resize
												_a	:    Dict         =		size
												_a	:    str         =		keep_aspect_ratio
												_a	:    Any         =		ensure_multiple_of
												_a	:    Optional[Any]         =		resample
												_a	:    List[Any]         =		do_rescale
												_a	:    int         =		rescale_factor
												_a	:    Any         =		do_normalize
												_a	:    Union[str, Any]         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a	:    List[str]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = False       ,							_a = 1       ,							_a = PILImageResampling.BICUBIC       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    str         =		get_size_dict(_a       )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
												_a	:    Optional[Any]         =		get_resize_output_image_size(
												    _a       ,							output_size=(size['''height'''], size['''width'''])       ,							keep_aspect_ratio=_a       ,							multiple=_a       ,							)
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												return normalize(_a       ,							mean=_a       ,							std=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							**_a       ,							)    ->    PIL.Image.Image:
												_a	:    Optional[int]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    Union[str, Any]         =		size if size is not None else self.size
												_a	:    str         =		get_size_dict(_a       )
												_a	:    List[Any]         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_a	:    Optional[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_a	:    str         =		resample if resample is not None else self.resample
												_a	:    str         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    Union[str, Any]         =		rescale_factor if rescale_factor is not None else self.rescale_factor
												_a	:    List[Any]         =		do_normalize if do_normalize is not None else self.do_normalize
												_a	:    str         =		image_mean if image_mean is not None else self.image_mean
												_a	:    Tuple         =		image_std if image_std is not None else self.image_std

												_a	:    Dict         =		make_list_of_images(_a       )

												if not valid_images(_a       ):
																	raise ValueError(
																	    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																	    '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

												if do_resize and size is None or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

												if do_rescale and rescale_factor is None:
																	raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

												# All transformations expect numpy arrays.
												_a	:    Dict         =		[to_numpy_array(_a       ) for image in images]

												if do_resize:
																	_a	:    int         =		[self.resize(image=_a       ,							size=_a       ,							resample=_a       ) for image in images]

												if do_rescale:
																	_a	:    Optional[Any]         =		[self.rescale(image=_a       ,							scale=_a       ) for image in images]

												if do_normalize:
																	_a	:    Optional[int]         =		[self.normalize(image=_a       ,							mean=_a       ,							std=_a       ) for image in images]

												_a	:    int         =		[to_channel_dimension_format(_a       ,							_a       ) for image in images]

												_a	:    Tuple         =		{'''pixel_values''': images}
												return BatchFeature(data=_a       ,							tensor_type=_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Any:
												_a	:    Optional[Any]         =		outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(_a       ) != len(_a       ):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

																	if is_torch_tensor(_a       ):
																						_a	:    List[Any]         =		target_sizes.numpy()

																	_a	:    str         =		[]

																	for idx in range(len(_a       )       ):
																						_a	:    str         =		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=_a       )
																						_a	:    Union[str, Any]         =		resized_logits[0].argmax(dim=0       )
																						semantic_segmentation.append(_a       )
												else:
																	_a	:    Tuple         =		logits.argmax(dim=1       )
																	_a	:    Union[str, Any]         =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

												return semantic_segmentation



