




from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int     ):
					return [
					    int(10_00 * (box[0] / width)     ),
					    int(10_00 * (box[1] / height)     ),
					    int(10_00 * (box[2] / width)     ),
					    int(10_00 * (box[3] / height)     ),
					]







def 	_a      (						SCREAMING_SNAKE_CASE_							:				np.ndarray      ,      SCREAMING_SNAKE_CASE_							:				Optional[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[str]     ):
					__lowerCAmelCase				   =       to_pil_image(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       pil_image.size
					__lowerCAmelCase				   =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_      ,      lang=SCREAMING_SNAKE_CASE_      ,      output_type="dict"      ,      config=SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

					return words, normalized_boxes


class 			a__    (					snake_case__      ):
						_a							:    Dict					=    ["""pixel_values"""]






						def __init__(   self		,			_A = True		,			_A = None		,			_A = PILImageResampling.BILINEAR		,			_A = True		,			_A = 1 / 2_5_5		,			_A = True		,			_A = None		,			_A = None		,			_A = True		,			_A = None		,			_A = ""		,			**_A		,			):




											"""simple docstring"""



											super().__init__(**_A			)
											__lowerCAmelCase				   =       size if size is not None else {"height": 2_2_4, "width": 2_2_4}
											__lowerCAmelCase				   =       get_size_dict(_A			)

											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       size
											__lowerCAmelCase				   =       resample
											__lowerCAmelCase				   =       do_rescale
											__lowerCAmelCase				   =       rescale_value
											__lowerCAmelCase				   =       do_normalize
											__lowerCAmelCase				   =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCAmelCase				   =       image_std if image_std is not None else IMAGENET_STANDARD_STD
											__lowerCAmelCase				   =       apply_ocr
											__lowerCAmelCase				   =       ocr_lang
											__lowerCAmelCase				   =       tesseract_config






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = PILImageResampling.BILINEAR		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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()}"""			)
											__lowerCAmelCase				   =       (size["height"], size["width"])
											return resize(_A		,			size=_A		,			resample=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											return rescale(_A		,			scale=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



											return normalize(_A		,			mean=_A		,			std=_A		,			data_format=_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None		,			_A=None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = ChannelDimension.FIRST		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       do_resize if do_resize is not None else self.do_resize
											__lowerCAmelCase				   =       size if size is not None else self.size
											__lowerCAmelCase				   =       get_size_dict(_A			)
											__lowerCAmelCase				   =       resample if resample is not None else self.resample
											__lowerCAmelCase				   =       do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase				   =       rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCAmelCase				   =       do_normalize if do_normalize is not None else self.do_normalize
											__lowerCAmelCase				   =       image_mean if image_mean is not None else self.image_mean
											__lowerCAmelCase				   =       image_std if image_std is not None else self.image_std
											__lowerCAmelCase				   =       apply_ocr if apply_ocr is not None else self.apply_ocr
											__lowerCAmelCase				   =       ocr_lang if ocr_lang is not None else self.ocr_lang
											__lowerCAmelCase				   =       tesseract_config if tesseract_config is not None else self.tesseract_config

											__lowerCAmelCase				   =       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."			)

											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("If do_normalize is True, image_mean and image_std must be specified."			)

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

											# Tesseract OCR to get words + normalized bounding boxes
											if apply_ocr:
																requires_backends(self		,			"pytesseract"			)
																__lowerCAmelCase				   =       []
																__lowerCAmelCase				   =       []
																for image in images:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       apply_tesseract(_A		,			_A		,			_A			)
																					words_batch.append(_A			)
																					boxes_batch.append(_A			)

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

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

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

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

											__lowerCAmelCase				   =       BatchFeature(data={"pixel_values": images}		,			tensor_type=_A			)

											if apply_ocr:
																__lowerCAmelCase				   =       words_batch
																__lowerCAmelCase				   =       boxes_batch
											return data









"""simple docstring"""






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


_lowercase : Optional[int]      						=						logging.get_logger(__name__)
_lowercase : Optional[int]      						=						{"vocab_file": "spiece.model"}

_lowercase : str      						=						{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_lowercase : Optional[Any]      						=						{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Dict,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  str=False,  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Optional[int]=None,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Dict,  )->					None:
								lowerCamelCase__		:		Any			={} if sp_model_kwargs is None else sp_model_kwargs

								lowerCamelCase__		:		Optional[int]			=kwargs.get('''name_or_path'''     )
								if name_or_path is None:
											logger.warning(
											    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
											    ''' you are testing the model, this can safely be ignored'''     )
											lowerCamelCase__		:		List[Any]			='''None'''

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								lowerCamelCase__		:		Tuple			='''<|endoftext|>''' if eos_token is None else eos_token
								lowerCamelCase__		:		Dict			='''<unk>''' if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
											lowerCamelCase__		:		int			=unk_token if pad_token is None else pad_token
											lowerCamelCase__		:		str			=eos_token if bos_token is None else bos_token
								else:
											lowerCamelCase__		:		Any			='''<pad>''' if pad_token is None else pad_token
											lowerCamelCase__		:		str			='''<s>''' if bos_token is None else bos_token

								super().__init__(
								    do_lower_case=lowerCamelCase,  remove_space=lowerCamelCase,  keep_accents=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  pad_token=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )

								lowerCamelCase__		:		Union[str, Any]			=do_lower_case
								lowerCamelCase__		:		Union[str, Any]			=remove_space
								lowerCamelCase__		:		int			=keep_accents
								lowerCamelCase__		:		Tuple			=vocab_file

								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# Used for whitespace normalization in input texts
								# fmt : off
								lowerCamelCase__		:		List[Any]			={''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								lowerCamelCase__		:		int			=re.compile(
								    F'''[{"".join(map(lowerCamelCase,  list(range(0,  9     )     ) + list(range(11,  32     )     ) + list(range(127,  160     )     ) + [160, 173, 8203]     )     )}]'''     )



					def __getstate__(       self							:  Any     )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=self.__dict__.copy()
								lowerCamelCase__		:		List[Any]			=None
								return state



					def __setstate__(       self							:  Optional[Any],  lowerCamelCase							:  Dict     )->					int:
								lowerCamelCase__		:		List[str]			=d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''     ):
											lowerCamelCase__		:		Union[str, Any]			={}

								lowerCamelCase__		:		str			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					@property
					# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
					def 		snake_case       (       self							:  List[str]     )->					int:
								return len(self.sp_model     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str     )->					str:
								lowerCamelCase__		:		Dict			=self.non_printing_characters_re.sub('''''',  lowerCamelCase     )

								# Normalize whitespaces
								lowerCamelCase__		:		List[Any]			=''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]     )

								# NFC Unicode normalization
								lowerCamelCase__		:		Union[str, Any]			=unicodedata.normalize('''NFC''',  lowerCamelCase     )
								return text



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					List[str]:
								lowerCamelCase__		:		int			=self.preprocess_text(lowerCamelCase     )
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					int:
								return self.sp_model.PieceToId(lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  int     )->					str:
								return self.sp_model.IdToPiece(lowerCamelCase     )



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  str     )->					str:
								return out_string



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  List[str]     )->					str:
								lowerCamelCase__		:		Dict			=[]
								lowerCamelCase__		:		int			=''''''
								lowerCamelCase__		:		Any			=False
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
														if not prev_is_special:
																	out_string += " "

														out_string += self.sp_model.decode(lowerCamelCase     ) + token
														lowerCamelCase__		:		Tuple			=True
														lowerCamelCase__		:		Tuple			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
														lowerCamelCase__		:		Optional[Any]			=False
								out_string += self.sp_model.decode(lowerCamelCase     )

								return out_string



					def 		snake_case       (       self							:  Union[str, Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		Tuple			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



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

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

								return (out_vocab_file,)



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Union[str, List[str]],  lowerCamelCase							:  Union[str, bool] = False     )->					Union[List[int], List[List[int]], "torch.Tensor"]:

								if isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		str			=self.preprocess_text(lowerCamelCase     )
											lowerCamelCase__		:		Union[str, Any]			=self.sp_model.encode(lowerCamelCase     )
								else:
											lowerCamelCase__		:		int			=[self.preprocess_text(lowerCamelCase     ) for t in text]
											lowerCamelCase__		:		Tuple			=self.sp_model.encode(lowerCamelCase     )

								if return_tensors is True or return_tensors == "pt":
											lowerCamelCase__		:		Union[str, Any]			=torch.tensor(lowerCamelCase     )

								return token_ids



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Union[int, List[int]]     )->					str:
								return self.sp_model.decode(lowerCamelCase     )




					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  "Conversation"     )->					List[int]:
								lowerCamelCase__		:		Any			=[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
								lowerCamelCase__		:		str			=(
								    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowerCamelCase     ) + F'''{self.bos_token}Bot:'''
								)
								return self.encode(text=lowerCamelCase     )




