'''simple docstring'''







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCamelCase_      	=	logging.get_logger(__name__)




@add_end_docstrings(__a					)
class _SCREAMING_SNAKE_CASE(							__a					):




						def __init__(    self :       Tuple							,							**UpperCamelCase_ :       Optional[int]      )     ->				int:
													super().__init__(**lowercase_      )
													requires_backends(self							,							'vision'      )
													requires_backends(self							,							'torch'      )

													if self.framework != "pt":
																				raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''      )

													self.check_model_type(lowercase_      )




						def      __lowerCamelCase       (    self :       Any							,							**UpperCamelCase_ :       Union[str, Any]      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {}
													SCREAMING_SNAKE_CASE__     :List[str]						    = {}
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {}
													# preprocess args
													if "points_per_batch" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Dict						    = kwargs['points_per_batch']
													if "points_per_crop" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs['points_per_crop']
													if "crops_n_layers" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs['crops_n_layers']
													if "crop_overlap_ratio" in kwargs:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = kwargs['crop_overlap_ratio']
													if "crop_n_points_downscale_factor" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Dict						    = kwargs['crop_n_points_downscale_factor']
													# postprocess args
													if "pred_iou_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Any						    = kwargs['pred_iou_thresh']
													if "stability_score_offset" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['stability_score_offset']
													if "mask_threshold" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = kwargs['mask_threshold']
													if "stability_score_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['stability_score_thresh']
													if "crops_nms_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['crops_nms_thresh']
													if "output_rle_mask" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['output_rle_mask']
													if "output_bboxes_mask" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = kwargs['output_bboxes_mask']
													return preprocess_kwargs, forward_params, postprocess_kwargs




						def __call__(    self :       Tuple							,							UpperCamelCase_ :       List[Any]							,							*UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       int=None							,							UpperCamelCase_ :       Optional[int]=None							,							**UpperCamelCase_ :       int      )     ->				Any:
													return super().__call__(lowercase_							,							*lowercase_							,							num_workers=lowercase_							,							batch_size=lowercase_							,							**lowercase_      )




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Optional[Any]=64							,							UpperCamelCase_ :       int = 0							,							UpperCamelCase_ :       float = 5_12 / 15_00							,							UpperCamelCase_ :       Optional[int] = 32							,							UpperCamelCase_ :       Optional[int] = 1							,							)     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :str						    = load_image(lowercase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.image_processor.size['longest_edge']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Tuple						    = self.image_processor.generate_crop_boxes(
													    lowercase_							,							lowercase_							,							lowercase_							,							lowercase_							,							lowercase_							,							lowercase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor(images=lowercase_							,							return_tensors='pt'      )

													with self.device_placement():
																				if self.framework == "pt":
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_inference_context()
																											with inference_context():
																																		SCREAMING_SNAKE_CASE__     :List[str]						    = self._ensure_tensor_on_device(lowercase_							,							device=self.device      )
																																		SCREAMING_SNAKE_CASE__     :Tuple						    = self.model.get_image_embeddings(model_inputs.pop('pixel_values'      )      )
																																		SCREAMING_SNAKE_CASE__     :str						    = image_embeddings

													SCREAMING_SNAKE_CASE__     :Dict						    = grid_points.shape[1]
													SCREAMING_SNAKE_CASE__     :Any						    = points_per_batch if points_per_batch is not None else n_points

													if points_per_batch <= 0:
																				raise ValueError(
																				    'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
																				    'To return all points at once, set points_per_batch to None'      )

													for i in range(0							,							lowercase_							,							lowercase_      ):
																				SCREAMING_SNAKE_CASE__     :int						    = grid_points[:, i : i + points_per_batch, :, :]
																				SCREAMING_SNAKE_CASE__     :Any						    = input_labels[:, i : i + points_per_batch]
																				SCREAMING_SNAKE_CASE__     :Dict						    = i == n_points - points_per_batch
																				yield {
																				    "input_points": batched_points,
																				    "input_labels": labels,
																				    "input_boxes": crop_boxes,
																				    "is_last": is_last,
																				    **model_inputs,
																				}




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       List[str]=0.88							,							UpperCamelCase_ :       Tuple=0.95							,							UpperCamelCase_ :       Any=0							,							UpperCamelCase_ :       Tuple=1							,							)     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :Dict						    = model_inputs.pop('input_boxes'      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model_inputs.pop('is_last'      )
													SCREAMING_SNAKE_CASE__     :Any						    = model_inputs.pop('original_sizes'      ).tolist()
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model_inputs.pop('reshaped_input_sizes'      ).tolist()

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.model(**lowercase_      )

													# post processing happens here in order to avoid CPU GPU copies of ALL the masks
													SCREAMING_SNAKE_CASE__     :str						    = model_outputs['pred_masks']
													SCREAMING_SNAKE_CASE__     :int						    = self.image_processor.post_process_masks(
													    lowercase_							,							lowercase_							,							lowercase_							,							lowercase_							,							binarize=lowercase_      )
													SCREAMING_SNAKE_CASE__     :int						    = model_outputs['iou_scores']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor.filter_masks(
													    masks[0]							,							iou_scores[0]							,							original_sizes[0]							,							input_boxes[0]							,							lowercase_							,							lowercase_							,							lowercase_							,							lowercase_							,							)
													return {
													    "masks": masks,
													    "is_last": is_last,
													    "boxes": boxes,
													    "iou_scores": iou_scores,
													}




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Optional[int]=False							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       int=0.7							,							)     ->				str:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = []
													SCREAMING_SNAKE_CASE__     :int						    = []
													SCREAMING_SNAKE_CASE__     :List[str]						    = []
													for model_output in model_outputs:
																				all_scores.append(model_output.pop('iou_scores'      )      )
																				all_masks.extend(model_output.pop('masks'      )      )
																				all_boxes.append(model_output.pop('boxes'      )      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.cat(lowercase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.cat(lowercase_      )
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.image_processor.post_process_for_mask_generation(
													    lowercase_							,							lowercase_							,							lowercase_							,							lowercase_      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = defaultdict(lowercase_      )
													for output in model_outputs:
																				for k, v in output.items():
																											extra[k].append(lowercase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = {}
													if output_rle_mask:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rle_mask

													if output_bboxes_mask:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = bounding_boxes

													return {"masks": output_masks, "scores": iou_scores, **optional, **extra}







'''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


__snake_case						  =		logging.get_logger(__name__)
__snake_case						  =		{"""vocab_file""": """spiece.model"""}

__snake_case						  =		{
    """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""",
    }
}

__snake_case						  =		{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class       _a    (		__a      ):


  """simple docstring"""

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




  def __init__(		self      :       str   ,				lowercase_      :       List[str]   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       List[str]=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Dict=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       List[Any]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Optional[Dict[str, Any]] = None   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       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"""      )
      lowercase_     =       """None"""

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

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

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

    # Used for whitespace normalization in input texts
    # fmt : off
    lowercase_     =       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    lowercase_     =       re.compile(
        F"""[{"".join(map(lowercase_   ,				list(range(0   ,				9      )      ) + list(range(11   ,				32      )      ) + list(range(127   ,				160      )      ) + [160, 173, 8_203]      )      )}]"""      )




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.__dict__.copy()
    lowercase_     =       None
    return state




  def __setstate__(		self      :       Tuple   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       d

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

    lowercase_     =       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 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

    # Normalize whitespaces
    lowercase_     =       """""".join([char if char not in self.whitespaces else """ """ for char in text]      )

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       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(lowercase_      ) + token
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       {self.convert_ids_to_tokens(lowercase_      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


    if not os.path.isdir(lowercase_      ):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
      return
    lowercase_     =       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:
        lowercase_     =       self.sp_model.serialized_model_proto()
        fi.write(lowercase_      )

    return (out_vocab_file,)




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, List[str]]   ,				lowercase_      :       Union[str, bool] = False      ):







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Union[int, List[int]]      ):







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


    lowercase_     =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    lowercase_     =       (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowercase_      ) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=lowercase_      )

