

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      '''▁'''

lowerCAmelCase_			      =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase_			      =      {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase_			      =      {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
lowerCAmelCase_			      =      ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Dict		     =							VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE	:  Dict		     =							PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE	:  Dict		     =							["input_ids", "attention_mask"]

			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]
			SCREAMING_SNAKE_CASE	:  List[int]		     =							[]







			def __init__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Tuple=None				,  _UpperCamelCase					:							str=None				,  _UpperCamelCase					:							Any="</s>"				,  _UpperCamelCase					:							int="</s>"				,  _UpperCamelCase					:							List[Any]="<s>"				,  _UpperCamelCase					:							Optional[int]="<unk>"				,  _UpperCamelCase					:							Optional[Any]="<pad>"				,  _UpperCamelCase					:							Tuple="<mask>"				,  _UpperCamelCase					:							Optional[Dict[str, Any]] = None				,  **_UpperCamelCase					:							List[Any]				,  ) ->None:
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_  =      AddedToken(_UpperCamelCase				,  lstrip=_UpperCamelCase				,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase				,  _UpperCamelCase				) else mask_token

									snake_case_  =      {} if sp_model_kwargs is None else sp_model_kwargs

									snake_case_  =      kwargs.get('''additional_special_tokens'''				,  []				)
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=_UpperCamelCase				,  tgt_lang=_UpperCamelCase				,  eos_token=_UpperCamelCase				,  unk_token=_UpperCamelCase				,  sep_token=_UpperCamelCase				,  cls_token=_UpperCamelCase				,  pad_token=_UpperCamelCase				,  mask_token=_UpperCamelCase				,  sp_model_kwargs=self.sp_model_kwargs				,  **_UpperCamelCase				,  )

									snake_case_  =      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(str(_UpperCamelCase				)				)
									snake_case_  =      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
									snake_case_  =      {'''<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
									snake_case_  =      1

									snake_case_  =      len(self.sp_model				)
									snake_case_  =      {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase				)
									}
									snake_case_  =      {v: k for k, v in self.lang_code_to_id.items()}
									snake_case_  =      len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
									snake_case_  =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

									snake_case_  =      src_lang if src_lang is not None else '''en_XX'''
									snake_case_  =      self.lang_code_to_id[self._src_lang]
									snake_case_  =      tgt_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			@property
			def 							snake_case__(	self					:							Optional[int]				) ->int:
									return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token







			@property
			def 							snake_case__(	self					:							Optional[int]				) ->str:
									return self._src_lang







			@src_lang.setter
			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      new_src_lang
									self.set_src_lang_special_tokens(self._src_lang				)







			def __getstate__(	self					:							List[str]				) ->Dict:
									snake_case_  =      self.__dict__.copy()
									snake_case_  =      None
									return state







			def __setstate__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				) ->None:
									snake_case_  =      d

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

									snake_case_  =      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(self.vocab_file				)







			def 							snake_case__(	self					:							Any				) ->Dict:
									snake_case_  =      {self.convert_ids_to_tokens(_UpperCamelCase				): i for i in range(self.vocab_size				)}
									vocab.update(self.added_tokens_encoder				)
									return vocab







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							str				) ->List[str]:
									return self.sp_model.encode(_UpperCamelCase				,  out_type=_UpperCamelCase				)







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							str				) ->int:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									snake_case_  =      self.sp_model.PieceToId(_UpperCamelCase				)

									# 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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							int				) ->str:
									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 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Optional[int]				) ->Any:
									snake_case_  =      []
									snake_case_  =      ''''''
									snake_case_  =      False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase				) + token
																					snake_case_  =      True
																					snake_case_  =      []
															else:
																					current_sub_tokens.append(_UpperCamelCase				)
																					snake_case_  =      False
									out_string += self.sp_model.decode(_UpperCamelCase				)
									return out_string.strip()







			def 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str] = None				) ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase				):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
															return
									snake_case_  =      os.path.join(
									    _UpperCamelCase				,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

									if os.path.abspath(self.vocab_file				) != os.path.abspath(_UpperCamelCase				) and os.path.isfile(self.vocab_file				):
															copyfile(self.vocab_file				,  _UpperCamelCase				)
									elif not os.path.isfile(self.vocab_file				):
															with open(_UpperCamelCase				,  '''wb'''				) as fi:
																					snake_case_  =      self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase				)

									return (out_vocab_file,)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				,  _UpperCamelCase					:							bool = False				) ->List[int]:

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

									snake_case_  =      [1] * len(self.prefix_tokens				)
									snake_case_  =      [1] * len(self.suffix_tokens				)
									if token_ids_a is None:
															return prefix_ones + ([0] * len(_UpperCamelCase				)) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCamelCase				)) + ([0] * len(_UpperCamelCase				)) + suffix_ones







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				) ->List[int]:
									if token_ids_a is None:
															return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str]				,  _UpperCamelCase					:							Optional[str]				,  **_UpperCamelCase					:							Dict				) ->List[str]:
									if src_lang is None or tgt_lang is None:
															raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
									snake_case_  =      src_lang
									snake_case_  =      self(_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors=_UpperCamelCase				,  **_UpperCamelCase				)
									snake_case_  =      self.convert_tokens_to_ids(_UpperCamelCase				)
									snake_case_  =      tgt_lang_id
									return inputs







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str = "en_XX"				,  _UpperCamelCase					:							Optional[List[str]] = None				,  _UpperCamelCase					:							str = "ro_RO"				,  **_UpperCamelCase					:							int				,  ) ->BatchEncoding:
									snake_case_  =      src_lang
									snake_case_  =      tgt_lang
									return super().prepare_seqaseq_batch(_UpperCamelCase				,  _UpperCamelCase				,  **_UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				) ->Optional[Any]:
									return self.set_src_lang_special_tokens(self.src_lang				)







			def 							snake_case__(	self					:							List[str]				) ->str:
									return self.set_tgt_lang_special_tokens(self.tgt_lang				)







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[src_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]







			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							str				) ->None:
									snake_case_  =      self.lang_code_to_id[tgt_lang]
									snake_case_  =      [self.cur_lang_code_id]
									snake_case_  =      [self.eos_token_id]



'''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 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

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

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

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

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						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	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

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

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

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



