




"""simple docstring"""






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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase = True						,   lowercase = None						,   lowercase = 32						,   lowercase = True						,   lowercase = 1 / 255						,   lowercase = True						,   lowercase = True						,   lowercase = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]						,   lowercase = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]						,   lowercase = True						,   lowercase=7						,   lowercase=30						,   lowercase=400						,   lowercase=3						,   ):
							_lowerCamelCase  :    List[str]					=					parent
							_lowerCamelCase  :    Any					=					do_resize
							_lowerCamelCase  :    List[Any]					=					size if size is not None else {'shortest_edge': 288}
							_lowerCamelCase  :    str					=					size_divisor
							_lowerCamelCase  :    Union[str, Any]					=					do_rescale
							_lowerCamelCase  :    int					=					rescale_factor
							_lowerCamelCase  :    List[Any]					=					do_normalize
							_lowerCamelCase  :    int					=					do_center_crop
							_lowerCamelCase  :    List[Any]					=					image_mean
							_lowerCamelCase  :    Tuple					=					image_std
							_lowerCamelCase  :    List[str]					=					do_pad
							_lowerCamelCase  :    Tuple					=					batch_size
							_lowerCamelCase  :    int					=					num_channels
							_lowerCamelCase  :    Any					=					min_resolution
							_lowerCamelCase  :    str					=					max_resolution
			def       A_					(						self						):
							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       A_					(						self						,   lowercase						,   lowercase=False						):
							if not batched:
											_lowerCamelCase  :    Any					=					self.size['shortest_edge']
											_lowerCamelCase  :    List[Any]					=					image_inputs[0]
											if isinstance(lowercase						,   Image.Image						):
															_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					image.size
											else:
															_lowerCamelCase,				_lowerCamelCase  :    str					=					image.shape[1], image.shape[2]
											_lowerCamelCase  :    Union[str, Any]					=					size / min(lowercase						,   lowercase						)
											if h < w:
															_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					size, scale * w
											else:
															_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					scale * h, size

											_lowerCamelCase  :    Union[str, Any]					=					int((1333 / 800) * size						)
											if max(lowercase						,   lowercase						) > max_size:
															_lowerCamelCase  :    Tuple					=					max_size / max(lowercase						,   lowercase						)
															_lowerCamelCase  :    Union[str, Any]					=					newh * scale
															_lowerCamelCase  :    Union[str, Any]					=					neww * scale

											_lowerCamelCase,				_lowerCamelCase  :    Dict					=					int(newh + 0.5						), int(neww + 0.5						)
											_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					(
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

							else:
											_lowerCamelCase  :    str					=					[]
											for image in image_inputs:
															_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					self.get_expected_values([image]						)
															expected_values.append((expected_height, expected_width)						)
											_lowerCamelCase  :    List[Any]					=					max(lowercase						,   key=lambda lowercase						: item[0]						)[0]
											_lowerCamelCase  :    Dict					=					max(lowercase						,   key=lambda lowercase						: item[1]						)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    BridgeTowerImageProcessor if is_vision_available() else None
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					BridgeTowerImageProcessingTester(self						)
			@property
			def       A_					(						self						):
							return self.image_processor_tester.prepare_image_processor_dict()
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					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       A_					(						self						):
							pass
			def       A_					(						self						):
							# Initialize image processor
							_lowerCamelCase  :    Optional[Any]					=					self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							_lowerCamelCase  :    Dict					=					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  :    Optional[Any]					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Optional[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  :    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       A_					(						self						):
							# Initialize image processor
							_lowerCamelCase  :    Dict					=					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  :    Union[str, Any]					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    List[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  :    List[Any]					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    int					=					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       A_					(						self						):
							# Initialize image processor
							_lowerCamelCase  :    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  :    str					=					image_processing(image_inputs[0]						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Dict					=					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  :    Dict					=					image_processing(lowercase						,   return_tensors='pt'						).pixel_values

							_lowerCamelCase,				_lowerCamelCase  :    Dict					=					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 shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		"""▁"""

lowercase__  		=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__  		=		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase = None						,   **lowercase						,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    str					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

							_lowerCamelCase  :    Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   sep_token=lowercase						,   cls_token=lowercase						,   pad_token=lowercase						,   mask_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Any					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(lowercase						)						)
							_lowerCamelCase  :    str					=					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_lowerCamelCase  :    List[str]					=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_lowerCamelCase  :    List[Any]					=					1

							_lowerCamelCase  :    List[Any]					=					len(self.sp_model						) + self.fairseq_offset
							_lowerCamelCase  :    int					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(						self						):
							_lowerCamelCase  :    Tuple					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.serialized_model_proto()
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					d

							# for backward compatibility
							if not hasattr(self						,   'sp_model_kwargs'						):
											_lowerCamelCase  :    str					=					{}

							_lowerCamelCase  :    List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)
			def       A_					(						self						,   lowercase						,   lowercase = None						):

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    List[Any]					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Tuple					=					[self.sep_token_id]
							_lowerCamelCase  :    Optional[int]					=					[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
			@property
			def       A_					(						self						):
							return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.PieceToId(lowercase						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
			def       A_					(						self						,   lowercase						):
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					''.join(lowercase						).replace(lowercase						,   ' '						).strip()
							return out_string






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    int					=					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						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file						,   lowercase						)
							elif not os.path.isfile(self.vocab_file						):
											with open(lowercase						,   'wb'						) as fi:
															_lowerCamelCase  :    str					=					self.sp_model.serialized_model_proto()
															fi.write(lowercase						)

							return (out_vocab_file,)