

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class        A_		(       unittest.TestCase				):
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			int = 3_2   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict=7   ,SCREAMING_SNAKE_CASE__		:			Dict=3_0   ,SCREAMING_SNAKE_CASE__		:			List[str]=4_0_0   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=3   ,):
								__lowerCamelCase				:				List[str]					       = parent
								__lowerCamelCase				:				Any					       = do_resize
								__lowerCamelCase				:				Tuple					       = size if size is not None else {'shortest_edge': 2_8_8}
								__lowerCamelCase				:				List[str]					       = size_divisor
								__lowerCamelCase				:				List[str]					       = do_rescale
								__lowerCamelCase				:				Tuple					       = rescale_factor
								__lowerCamelCase				:				Optional[Any]					       = do_normalize
								__lowerCamelCase				:				List[str]					       = do_center_crop
								__lowerCamelCase				:				Dict					       = image_mean
								__lowerCamelCase				:				Any					       = image_std
								__lowerCamelCase				:				Dict					       = do_pad
								__lowerCamelCase				:				Dict					       = batch_size
								__lowerCamelCase				:				int					       = num_channels
								__lowerCamelCase				:				List[Any]					       = min_resolution
								__lowerCamelCase				:				Tuple					       = max_resolution
							def     lowerCAmelCase     (		self		:			str):
								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		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[Any]=False):
								if not batched:
									__lowerCamelCase				:				List[Any]					       = self.size['shortest_edge']
									__lowerCamelCase				:				Optional[Any]					       = image_inputs[0]
									if isinstance(lowercase_   ,Image.Image):
										__lowerCamelCase       , __lowerCamelCase				:				Dict					       = image.size
									else:
										__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = image.shape[1], image.shape[2]
									__lowerCamelCase				:				Dict					       = size / min(lowercase_   ,lowercase_)
									if h < w:
										__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = size, scale * w
									else:
										__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = scale * h, size

									__lowerCamelCase				:				List[Any]					       = int((1_3_3_3 / 8_0_0) * size)
									if max(lowercase_   ,lowercase_) > max_size:
										__lowerCamelCase				:				Union[str, Any]					       = max_size / max(lowercase_   ,lowercase_)
										__lowerCamelCase				:				Dict					       = newh * scale
										__lowerCamelCase				:				Union[str, Any]					       = neww * scale

									__lowerCamelCase       , __lowerCamelCase				:				Union[str, Any]					       = int(newh + 0.5), int(neww + 0.5)
									__lowerCamelCase       , __lowerCamelCase				:				int					       = (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

								else:
									__lowerCamelCase				:				List[str]					       = []
									for image in image_inputs:
										__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
									__lowerCamelCase				:				Union[str, Any]					       = max(lowercase_   ,key=lambda SCREAMING_SNAKE_CASE__: item[0])[0]
									__lowerCamelCase				:				int					       = max(lowercase_   ,key=lambda SCREAMING_SNAKE_CASE__: item[1])[1]

								return expected_height, expected_width






@require_torch
@require_vision
class        A_		(       _lowerCamelCase			,							unittest.TestCase				):
							_UpperCAmelCase    :  str						      =      BridgeTowerImageProcessor if is_vision_available() else None
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Optional[int]					       = BridgeTowerImageProcessingTester(self)
							@property
							def     lowerCAmelCase     (		self		:			Any):
								return self.image_processor_tester.prepare_image_processor_dict()
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				Optional[int]					       = 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		:			Union[str, Any]):
								pass
							def     lowerCAmelCase     (		self		:			List[Any]):
								# Initialize image processor
								__lowerCamelCase				:				Tuple					       = self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase				:				Optional[int]					       = prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_)
								for image in image_inputs:
									self.assertIsInstance(lowercase_   ,Image.Image)

								# Test not batched input
								__lowerCamelCase				:				Union[str, Any]					       = image_processing(image_inputs[0]   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				int					       = 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
								__lowerCamelCase				:				Union[str, Any]					       = image_processing(lowercase_   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				Any					       = 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		:			Dict):
								# Initialize image processor
								__lowerCamelCase				:				Optional[int]					       = self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase				:				Dict					       = 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
								__lowerCamelCase				:				Optional[int]					       = image_processing(image_inputs[0]   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				Tuple					       = 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
								__lowerCamelCase				:				List[Any]					       = image_processing(lowercase_   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = 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		:			Any):
								# Initialize image processor
								__lowerCamelCase				:				Optional[int]					       = self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase				:				int					       = 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
								__lowerCamelCase				:				Union[str, Any]					       = image_processing(image_inputs[0]   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				Any					       = 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
								__lowerCamelCase				:				str					       = image_processing(lowercase_   ,return_tensors='pt').pixel_values

								__lowerCamelCase       , __lowerCamelCase				:				Union[str, Any]					       = 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
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							a  :				Dict									=  None


a  :				List[Any]									=  logging.get_logger(__name__)

a  :				List[Any]									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a  :				str									=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a  :				List[Any]									=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]
				snake_case_      =		TaTokenizer

				snake_case_      =		[]


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					List[Any]="<unk>"       ,				lowercase_    :					int="<pad>"       ,				lowercase_    :					int=100       ,				lowercase_    :					List[Any]=None       ,				**lowercase_    :					List[str]       ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
														snake_case_									=				[F"<extra_id_{i}>" for i in range(lowercase_					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														snake_case_									=				len(set(filter(lambda lowercase_					: bool('''extra_id_''' in str(lowercase_					)					)       ,				lowercase_					)					)					)
														if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																			    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																			    ''' tokens'''					)

									super().__init__(
									    lowercase_       ,				tokenizer_file=lowercase_       ,				eos_token=lowercase_       ,				unk_token=lowercase_       ,				pad_token=lowercase_       ,				extra_ids=lowercase_       ,				additional_special_tokens=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				False if not self.vocab_file else True
									snake_case_									=				extra_ids


				@staticmethod
				def   A_ (		lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														snake_case_									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
														elif init_max_model_length is None:
																			warnings.warn(
																			    '''This tokenizer was incorrectly instantiated with a model max length of'''
																			    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																			    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																			    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																			    F" {pretrained_model_name_or_path} automatically truncating your input to"
																			    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																			    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																			    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																			    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,				lowercase_       ,				)

									return max_model_length


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''					)

									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
														copyfile(self.vocab_file       ,				lowercase_					)
														logger.info(F"Copy vocab file to {out_vocab_file}"					)

									return (out_vocab_file,)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
									else:
														snake_case_									=				token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a


				def   A_ (		self    :					int       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.eos_token_id]

									if token_ids_a is None:
														return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


				def   A_ (		self    :					Dict					):
									return list(
									    set(filter(lambda lowercase_					: bool(re.search(R'''<extra_id_\d+>'''       ,				lowercase_					)					) is not None       ,				self.additional_special_tokens					)					)					)


				def   A_ (		self    :					Any					):
									return [self.convert_tokens_to_ids(lowercase_					) for token in self.get_sentinel_tokens()]

