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


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					def constraint_to_multiple_of(__snake_case  ,     __snake_case  ,     __snake_case=0  ,     __snake_case=None						):
										_UpperCamelCase			     =     round(val / multiple						) * multiple

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

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

										return x

					_UpperCamelCase			     =     (output_size, output_size) if isinstance(__snake_case  ,     __snake_case						) else output_size

					_UpperCamelCase     ,    _UpperCamelCase			     =     get_image_size(__snake_case						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     output_size

					# determine new height and width
					_UpperCamelCase			     =     output_height / input_height
					_UpperCamelCase			     =     output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width						) < abs(1 - scale_height						):
															# fit width
															_UpperCamelCase			     =     scale_width
										else:
															# fit height
															_UpperCamelCase			     =     scale_height

					_UpperCamelCase			     =     constraint_to_multiple_of(scale_height * input_height  ,     multiple=__snake_case						)
					_UpperCamelCase			     =     constraint_to_multiple_of(scale_width * input_width  ,     multiple=__snake_case						)

					return (new_height, new_width)







class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     ["pixel_values"]







							def __init__(   self      :     List[Any] ,			_A      :     bool = True ,			_A      :     Dict[str, int] = None ,			_A      :     PILImageResampling = PILImageResampling.BILINEAR ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     bool = True ,			_A      :     Union[int, float] = 1 / 255 ,			_A      :     bool = True ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			**_A      :     List[str] ,			):
												super().__init__(**_A		)
												_UpperCamelCase			     =     size if size is not None else {'''height''': 384, '''width''': 384}
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     do_resize
												_UpperCamelCase			     =     size
												_UpperCamelCase			     =     keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of
												_UpperCamelCase			     =     resample
												_UpperCamelCase			     =     do_rescale
												_UpperCamelCase			     =     rescale_factor
												_UpperCamelCase			     =     do_normalize
												_UpperCamelCase			     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_UpperCamelCase			     =     image_std if image_std is not None else IMAGENET_STANDARD_STD







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     np.ndarray ,			_A      :     Dict[str, int] ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     PILImageResampling = PILImageResampling.BICUBIC ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												_UpperCamelCase			     =     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()}"""		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     str ,			_A      :     np.ndarray ,			_A      :     Union[int, float] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return rescale(_A ,			scale=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     np.ndarray ,			_A      :     Union[float, List[float]] ,			_A      :     Union[float, List[float]] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return normalize(_A ,			mean=_A ,			std=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     ImageInput ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     PILImageResampling = None ,			_A      :     bool = None ,			_A      :     float = None ,			_A      :     bool = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[str, TensorType]] = None ,			_A      :     ChannelDimension = ChannelDimension.FIRST ,			**_A      :     str ,			):
												_UpperCamelCase			     =     do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase			     =     size if size is not None else self.size
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_UpperCamelCase			     =     resample if resample is not None else self.resample
												_UpperCamelCase			     =     do_rescale if do_rescale is not None else self.do_rescale
												_UpperCamelCase			     =     rescale_factor if rescale_factor is not None else self.rescale_factor
												_UpperCamelCase			     =     do_normalize if do_normalize is not None else self.do_normalize
												_UpperCamelCase			     =     image_mean if image_mean is not None else self.image_mean
												_UpperCamelCase			     =     image_std if image_std is not None else self.image_std

												_UpperCamelCase			     =     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.
												_UpperCamelCase			     =     [to_numpy_array(_A		) for image in images]

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

												if do_rescale:
																	_UpperCamelCase			     =     [self.rescale(image=_A ,			scale=_A		) for image in images]

												if do_normalize:
																	_UpperCamelCase			     =     [self.normalize(image=_A ,			mean=_A ,			std=_A		) for image in images]

												_UpperCamelCase			     =     [to_channel_dimension_format(_A ,			_A		) for image in images]

												_UpperCamelCase			     =     {'''pixel_values''': images}
												return BatchFeature(data=_A ,			tensor_type=_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     Any ,			_A      :     List[Tuple] = None		):
												_UpperCamelCase			     =     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		):
																						_UpperCamelCase			     =     target_sizes.numpy()

																	_UpperCamelCase			     =     []

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

												return semantic_segmentation

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase       =		"▁"

_lowerCAmelCase       =		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     BertGenerationTokenizer
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     True







							def  UpperCamelCase_						(   self      :     List[str]		):
												super().setUp()

												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)
												tokenizer.save_pretrained(self.tmpdirname		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     '''<s>'''
												_UpperCamelCase			     =     1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A		) ,			_A		)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A		) ,			_A		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     list(self.get_tokenizer().get_vocab().keys()		)

												self.assertEqual(vocab_keys[0] ,			'''<unk>'''		)
												self.assertEqual(vocab_keys[1] ,			'''<s>'''		)
												self.assertEqual(vocab_keys[-1] ,			'''<pad>'''		)
												self.assertEqual(len(_A		) ,			1002		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.assertEqual(self.get_tokenizer().vocab_size ,			1000		)







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)

												_UpperCamelCase			     =     tokenizer.tokenize('''This is a test'''		)
												self.assertListEqual(_A ,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']		)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_A		) ,			[285, 46, 10, 170, 382] ,			)

												_UpperCamelCase			     =     tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
												self.assertListEqual(
												    _A ,			[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''9''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''é''',
												        '''.''',
												    ] ,			)
												_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(_A		)
												self.assertListEqual(
												    _A ,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,			)

												_UpperCamelCase			     =     tokenizer.convert_ids_to_tokens(_A		)
												self.assertListEqual(
												    _A ,			[
												        SPIECE_UNDERLINE + '''I''',
												        SPIECE_UNDERLINE + '''was''',
												        SPIECE_UNDERLINE + '''b''',
												        '''or''',
												        '''n''',
												        SPIECE_UNDERLINE + '''in''',
												        SPIECE_UNDERLINE + '''''',
												        '''<unk>''',
												        '''2''',
												        '''0''',
												        '''0''',
												        '''0''',
												        ''',''',
												        SPIECE_UNDERLINE + '''and''',
												        SPIECE_UNDERLINE + '''this''',
												        SPIECE_UNDERLINE + '''is''',
												        SPIECE_UNDERLINE + '''f''',
												        '''al''',
												        '''s''',
												        '''<unk>''',
												        '''.''',
												    ] ,			)







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)







							@slow
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     '''Hello World!'''
												_UpperCamelCase			     =     [1_8536, 2260, 101]

												self.assertListEqual(_A ,			self.big_tokenizer.encode(_A		)		)







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     (
												    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
												    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
												)
												_UpperCamelCase			     =     [
												    871,
												    419,
												    358,
												    946,
												    991,
												    2521,
												    452,
												    358,
												    1357,
												    387,
												    7751,
												    3536,
												    112,
												    985,
												    456,
												    126,
												    865,
												    938,
												    5400,
												    5734,
												    458,
												    1368,
												    467,
												    786,
												    2462,
												    5246,
												    1159,
												    633,
												    865,
												    4519,
												    457,
												    582,
												    852,
												    2557,
												    427,
												    916,
												    508,
												    405,
												    3_4324,
												    497,
												    391,
												    408,
												    1_1342,
												    1244,
												    385,
												    100,
												    938,
												    985,
												    456,
												    574,
												    362,
												    1_2597,
												    3200,
												    3129,
												    1172,
												]

												self.assertListEqual(_A ,			self.big_tokenizer.encode(_A		)		)







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												import torch

												from transformers import BertGenerationConfig, BertGenerationEncoder

												# Build sequence
												_UpperCamelCase			     =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
												_UpperCamelCase			     =     ''' '''.join(_A		)
												_UpperCamelCase			     =     self.big_tokenizer.encode_plus(_A ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)
												_UpperCamelCase			     =     self.big_tokenizer.batch_encode_plus(
												    [sequence + ''' ''' + sequence] ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)

												_UpperCamelCase			     =     BertGenerationConfig()
												_UpperCamelCase			     =     BertGenerationEncoder(_A		)

												assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

												with torch.no_grad():
																	model(**_A		)
																	model(**_A		)







							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												# fmt: off
												_UpperCamelCase			     =     {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=_A ,			model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' ,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' ,			)
