





'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Tuple					,       __lowerCamelCase :    int    )						->Optional[int]:
    return [
        int(1000 * (box[0] / width)    ),
        int(1000 * (box[1] / height)    ),
        int(1000 * (box[2] / width)    ),
        int(1000 * (box[3] / height)    ),
    ]





def lowerCamelCase						(     __lowerCamelCase :    np.ndarray					,       __lowerCamelCase :    Optional[str]					,       __lowerCamelCase :    Optional[str] = None    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			tesseract_config if tesseract_config is not None else """"""

    # apply OCR
    _SCREAMING_SNAKE_CASE									=			to_pil_image(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			pil_image.size
    _SCREAMING_SNAKE_CASE									=			pytesseract.image_to_data(__lowerCamelCase					,       lang=__lowerCamelCase					,       output_type="""dict"""					,       config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    _SCREAMING_SNAKE_CASE									=			[idx for idx, word in enumerate(__lowerCamelCase    ) if not word.strip()]
    _SCREAMING_SNAKE_CASE									=			[word for idx, word in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]
    _SCREAMING_SNAKE_CASE									=			[coord for idx, coord in enumerate(__lowerCamelCase    ) if idx not in irrelevant_indices]

    # turn coordinates into (left, top, left+width, top+height) format
    _SCREAMING_SNAKE_CASE									=			[]
    for x, y, w, h in zip(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			[x, y, x + w, y + h]
        actual_boxes.append(__lowerCamelCase    )

    # finally, normalize the bounding boxes
    _SCREAMING_SNAKE_CASE									=			[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )    )

    assert len(__lowerCamelCase    ) == len(__lowerCamelCase    ), "Not as many words as there are bounding boxes"

    return words, normalized_boxes






class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = ['''pixel_values''']


    def __init__(		self ,		A = True ,		A = None ,		A = PILImageResampling.BILINEAR ,		A = True ,		A = None ,		A = "" ,		**A ,		)		->   None:
        super().__init__(**A       )
        _SCREAMING_SNAKE_CASE									=			size if size is not None else {"""height""": 224, """width""": 224}
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )

        _SCREAMING_SNAKE_CASE									=			do_resize
        _SCREAMING_SNAKE_CASE									=			size
        _SCREAMING_SNAKE_CASE									=			resample
        _SCREAMING_SNAKE_CASE									=			apply_ocr
        _SCREAMING_SNAKE_CASE									=			ocr_lang
        _SCREAMING_SNAKE_CASE									=			tesseract_config


    def 			snake_case_(		self ,		A ,		A ,		A = PILImageResampling.BILINEAR ,		A = None ,		**A ,		)		->   np.ndarray:
        _SCREAMING_SNAKE_CASE									=			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()}'       )
        _SCREAMING_SNAKE_CASE									=			(size["""height"""], size["""width"""])
        return resize(A ,		size=A ,		resample=A ,		data_format=A ,		**A       )





    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = None ,		A = ChannelDimension.FIRST ,		**A ,		)		->   PIL.Image.Image:
        _SCREAMING_SNAKE_CASE									=			do_resize if do_resize is not None else self.do_resize
        _SCREAMING_SNAKE_CASE									=			size if size is not None else self.size
        _SCREAMING_SNAKE_CASE									=			get_size_dict(A       )
        _SCREAMING_SNAKE_CASE									=			resample if resample is not None else self.resample
        _SCREAMING_SNAKE_CASE									=			apply_ocr if apply_ocr is not None else self.apply_ocr
        _SCREAMING_SNAKE_CASE									=			ocr_lang if ocr_lang is not None else self.ocr_lang
        _SCREAMING_SNAKE_CASE									=			tesseract_config if tesseract_config is not None else self.tesseract_config

        _SCREAMING_SNAKE_CASE									=			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:
            raise ValueError("""Size must be specified if do_resize is True."""       )

        # All transformations expect numpy arrays.
        _SCREAMING_SNAKE_CASE									=			[to_numpy_array(A       ) for image in images]

        if apply_ocr:
            requires_backends(self ,		"""pytesseract"""       )
            _SCREAMING_SNAKE_CASE									=			[]
            _SCREAMING_SNAKE_CASE									=			[]
            for image in images:
                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			apply_tesseract(A ,		A ,		A       )
                words_batch.append(A       )
                boxes_batch.append(A       )

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

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        _SCREAMING_SNAKE_CASE									=			[flip_channel_order(A       ) for image in images]
        _SCREAMING_SNAKE_CASE									=			[to_channel_dimension_format(A ,		A       ) for image in images]

        _SCREAMING_SNAKE_CASE									=			BatchFeature(data={"""pixel_values""": images} ,		tensor_type=A       )

        if apply_ocr:
            _SCREAMING_SNAKE_CASE									=			words_batch
            _SCREAMING_SNAKE_CASE									=			boxes_batch
        return data




"""simple docstring"""







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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       __SCREAMING_SNAKE_CASE    	=None


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    	={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE    	={
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE    	={
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE    	=["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"]







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  ['input_ids', 'attention_mask']
   lowercase							=  MBartTokenizer

   lowercase							=  []
   lowercase							=  []
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="<unk>"     ,__UpperCamelCase="<pad>"     ,__UpperCamelCase="<mask>"     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				str													=	AddedToken(__UpperCamelCase     ,lstrip=__UpperCamelCase     ,rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else mask_token

        super().__init__(
            vocab_file=__UpperCamelCase     ,tokenizer_file=__UpperCamelCase     ,bos_token=__UpperCamelCase     ,eos_token=__UpperCamelCase     ,sep_token=__UpperCamelCase     ,cls_token=__UpperCamelCase     ,unk_token=__UpperCamelCase     ,pad_token=__UpperCamelCase     ,mask_token=__UpperCamelCase     ,src_lang=__UpperCamelCase     ,tgt_lang=__UpperCamelCase     ,additional_special_tokens=__UpperCamelCase     ,**__UpperCamelCase     ,)

        lowercase_	:				str													=	vocab_file
        lowercase_	:				Optional[Any]													=	False if not self.vocab_file else True

        lowercase_	:				List[str]													=	FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
        lowercase_	:				List[Any]													=	{
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_	:				Dict													=	src_lang if src_lang is not None else 'en_XX'
        lowercase_	:				Union[str, Any]													=	self.convert_tokens_to_ids(self._src_lang			)
        lowercase_	:				int													=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	[self.sep_token_id]
        lowercase_	:				str													=	[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]
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
        lowercase_	:				Dict													=	src_lang
        lowercase_	:				List[Any]													=	self(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Dict													=	tgt_lang_id
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = "en_XX"     ,__UpperCamelCase = None     ,__UpperCamelCase = "ro_RO"     ,**__UpperCamelCase     ,)	->      BatchEncoding:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	src_lang
        lowercase_	:				List[Any]													=	tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Any													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	[]
        lowercase_	:				int													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				Any													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				int													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				Union[str, Any]													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				str													=	[]
        lowercase_	:				Dict													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				str													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				List[Any]													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				int													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Tuple[str]:


        '''simple docstring'''


        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(__UpperCamelCase			):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''			)
             return
        lowercase_	:				Tuple													=	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			):
             copyfile(self.vocab_file     ,__UpperCamelCase			)

        return (out_vocab_file,)

