



"""simple docstring"""


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  Dict    ,   _SCREAMING_SNAKE_CASE      :  int    ,   _SCREAMING_SNAKE_CASE      :  List[Any]=True    ,   _SCREAMING_SNAKE_CASE      :  Optional[int]="pt" ):






				'''simple docstring'''



				_UpperCAmelCase							=			{'''add_prefix_space''': True} if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase ) and not line.startswith(''' ''' ) else {}
				_UpperCAmelCase							=			padding_side
				return tokenizer(
				    [line]    ,   max_length=_UpperCAmelCase    ,   padding='''max_length''' if pad_to_max_length else None    ,   truncation=_UpperCAmelCase    ,   return_tensors=_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  Any=None    ,   ):






				'''simple docstring'''



				_UpperCAmelCase							=			input_ids.ne(_UpperCAmelCase ).any(dim=0 )
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 	_a       (    __UpperCAmelCase):
			"""simple docstring"""



			def __init__(						self   :			Optional[int] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			List[str] ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			Any="train" ,      __UpperCamelCase   :			Any=None ,      __UpperCamelCase   :			int=None ,      __UpperCamelCase   :			Optional[Any]=None ,      __UpperCamelCase   :			Any="" ,      )->Optional[Any]:
							super().__init__()
							_UpperCAmelCase							=			Path(__SCREAMING_SNAKE_CASE      ).joinpath(type_path + '''.source'''      )
							_UpperCAmelCase							=			Path(__SCREAMING_SNAKE_CASE      ).joinpath(type_path + '''.target'''      )
							_UpperCAmelCase							=			self.get_char_lens(self.src_file      )
							_UpperCAmelCase							=			max_source_length
							_UpperCAmelCase							=			max_target_length
							assert min(self.src_lens      ) > 0, F'found empty line in {self.src_file}'
							_UpperCAmelCase							=			tokenizer
							_UpperCAmelCase							=			prefix
							if n_obs is not None:
											_UpperCAmelCase							=			self.src_lens[:n_obs]
							_UpperCAmelCase							=			src_lang
							_UpperCAmelCase							=			tgt_lang



			def __len__(						self   :			Any      )->Union[str, Any]:
							return len(self.src_lens      )



			def __getitem__(						self   :			Tuple ,      __UpperCamelCase   :			Dict      )->Tuple:
							_UpperCAmelCase							=			index + 1  # linecache starts at 1
							_UpperCAmelCase							=			self.prefix + linecache.getline(str(self.src_file      ) ,      __SCREAMING_SNAKE_CASE      ).rstrip('''\n'''      )
							_UpperCAmelCase							=			linecache.getline(str(self.tgt_file      ) ,      __SCREAMING_SNAKE_CASE      ).rstrip('''\n'''      )
							assert source_line, F'empty source line for index {index}'
							assert tgt_line, F'empty tgt line for index {index}'

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer ,      __SCREAMING_SNAKE_CASE      ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							_UpperCAmelCase							=			(
							    self.tokenizer.question_encoder if isinstance(self.tokenizer ,      __SCREAMING_SNAKE_CASE      ) else self.tokenizer
							)
							_UpperCAmelCase							=			self.tokenizer.generator if isinstance(self.tokenizer ,      __SCREAMING_SNAKE_CASE      ) else self.tokenizer

							_UpperCAmelCase							=			encode_line(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      self.max_source_length ,      '''right'''      )
							_UpperCAmelCase							=			encode_line(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      self.max_target_length ,      '''right'''      )

							_UpperCAmelCase							=			source_inputs['''input_ids'''].squeeze()
							_UpperCAmelCase							=			target_inputs['''input_ids'''].squeeze()
							_UpperCAmelCase							=			source_inputs['''attention_mask'''].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}



			@staticmethod
			def        lowercase__  (						__UpperCamelCase   :			Tuple      )->int:
							return [len(__SCREAMING_SNAKE_CASE      ) for x in Path(__SCREAMING_SNAKE_CASE      ).open().readlines()]



			def        lowercase__  (						self   :			Optional[Any] ,      __UpperCamelCase   :			List[Any]      )->List[str]:
							_UpperCAmelCase							=			torch.stack([x['''input_ids'''] for x in batch]      )
							_UpperCAmelCase							=			torch.stack([x['''attention_mask'''] for x in batch]      )
							_UpperCAmelCase							=			torch.stack([x['''decoder_input_ids'''] for x in batch]      )
							_UpperCAmelCase							=			(
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer ,      __SCREAMING_SNAKE_CASE      )
							    else self.tokenizer.pad_token_id
							)
							_UpperCAmelCase							=			(
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer ,      __SCREAMING_SNAKE_CASE      )
							    else self.tokenizer.pad_token_id
							)
							_UpperCAmelCase							=			trim_batch(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE      )
							_UpperCAmelCase						,  _UpperCAmelCase							=			trim_batch(__SCREAMING_SNAKE_CASE ,      __SCREAMING_SNAKE_CASE ,      attention_mask=__SCREAMING_SNAKE_CASE      )
							_UpperCAmelCase							=			{
							    '''input_ids''': source_ids,
							    '''attention_mask''': source_mask,
							    '''decoder_input_ids''': y,
							}
							return batch



__A      :							Optional[int]   =      getLogger(__name__)







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Union[str, Any] ):






				'''simple docstring'''



				return list(itertools.chain.from_iterable(_UpperCAmelCase ) )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Tuple ):






				'''simple docstring'''



				_UpperCAmelCase							=			get_git_info()
				save_json(_UpperCAmelCase    ,   os.path.join(_UpperCAmelCase    ,   '''git_log.json''' ) )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Dict    ,   _SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  Any=4    ,   **_SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



				with open(_UpperCAmelCase    ,   '''w''' ) as f:
								json.dump(_UpperCAmelCase    ,   _UpperCAmelCase    ,   indent=_UpperCAmelCase    ,   **_UpperCAmelCase )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Union[str, Any] ):






				'''simple docstring'''



				with open(_UpperCAmelCase ) as f:
								return json.load(_UpperCAmelCase )







def        lowercase		(					):






				'''simple docstring'''



				_UpperCAmelCase							=			git.Repo(search_parent_directories=_UpperCAmelCase )
				_UpperCAmelCase							=			{
				    '''repo_id''': str(_UpperCAmelCase ),
				    '''repo_sha''': str(repo.head.object.hexsha ),
				    '''repo_branch''': str(repo.active_branch ),
				    '''hostname''': str(socket.gethostname() ),
				}
				return repo_infos







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  List[Any]    ,   _SCREAMING_SNAKE_CASE      :  Dict ):






				'''simple docstring'''



				return list(map(_UpperCAmelCase    ,   _UpperCAmelCase ) )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Dict    ,   _SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



				with open(_UpperCAmelCase    ,   '''wb''' ) as f:
								return pickle.dump(_UpperCAmelCase    ,   _UpperCAmelCase )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Union[str, Any] ):






				'''simple docstring'''



				def remove_articles(_SCREAMING_SNAKE_CASE      :  Optional[Any] ):
								return re.sub(r'''\b(a|an|the)\b'''    ,   ''' '''    ,   _UpperCAmelCase )

				def white_space_fix(_SCREAMING_SNAKE_CASE      :  str ):
								return " ".join(text.split() )

				def remove_punc(_SCREAMING_SNAKE_CASE      :  Union[str, Any] ):
								_UpperCAmelCase							=			set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(_SCREAMING_SNAKE_CASE      :  Union[str, Any] ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase ) ) ) )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  int    ,   _SCREAMING_SNAKE_CASE      :  Any ):






				'''simple docstring'''



				_UpperCAmelCase							=			normalize_answer(_UpperCAmelCase ).split()
				_UpperCAmelCase							=			normalize_answer(_UpperCAmelCase ).split()
				_UpperCAmelCase							=			Counter(_UpperCAmelCase ) & Counter(_UpperCAmelCase )
				_UpperCAmelCase							=			sum(common.values() )
				if num_same == 0:
								return 0
				_UpperCAmelCase							=			1.0 * num_same / len(_UpperCAmelCase )
				_UpperCAmelCase							=			1.0 * num_same / len(_UpperCAmelCase )
				_UpperCAmelCase							=			(2 * precision * recall) / (precision + recall)
				return fa







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[int]    ,   _SCREAMING_SNAKE_CASE      :  Tuple ):






				'''simple docstring'''



				return normalize_answer(_UpperCAmelCase ) == normalize_answer(_UpperCAmelCase )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  str    ,   _SCREAMING_SNAKE_CASE      :  Dict ):






				'''simple docstring'''



				assert len(_UpperCAmelCase ) == len(_UpperCAmelCase )
				_UpperCAmelCase							=			0
				for hypo, pred in zip(_UpperCAmelCase    ,   _UpperCAmelCase ):
								em += exact_match_score(_UpperCAmelCase    ,   _UpperCAmelCase )
				if len(_UpperCAmelCase ) > 0:
								em /= len(_UpperCAmelCase )
				return {"em": em}







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any ):






				'''simple docstring'''



				return model_prefix.startswith('''rag''' )







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



				_UpperCAmelCase							=			{p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				_UpperCAmelCase							=			'''dropout_rate'''
				for p in extra_params:
								if getattr(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase ):
												if not hasattr(_UpperCAmelCase    ,   _UpperCAmelCase ) and not hasattr(_UpperCAmelCase    ,   equivalent_param[p] ):
																logger.info('''config doesn\'t have a `{}` attribute'''.format(_UpperCAmelCase ) )
																delattr(_UpperCAmelCase    ,   _UpperCAmelCase )
																continue
												_UpperCAmelCase							=			p if hasattr(_UpperCAmelCase    ,   _UpperCAmelCase ) else equivalent_param[p]
												setattr(_UpperCAmelCase    ,   _UpperCAmelCase    ,   getattr(_UpperCAmelCase    ,   _UpperCAmelCase ) )
												delattr(_UpperCAmelCase    ,   _UpperCAmelCase )
				return hparams, config








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

__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           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(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				{}
           __a						       =				{}
           __a						       =				{}
           # preprocess args
           if "points_per_batch" in kwargs:
               __a						       =				kwargs['''points_per_batch''']
           if "points_per_crop" in kwargs:
               __a						       =				kwargs['''points_per_crop''']
           if "crops_n_layers" in kwargs:
               __a						       =				kwargs['''crops_n_layers''']
           if "crop_overlap_ratio" in kwargs:
               __a						       =				kwargs['''crop_overlap_ratio''']
           if "crop_n_points_downscale_factor" in kwargs:
               __a						       =				kwargs['''crop_n_points_downscale_factor''']
           # postprocess args
           if "pred_iou_thresh" in kwargs:
               __a						       =				kwargs['''pred_iou_thresh''']
           if "stability_score_offset" in kwargs:
               __a						       =				kwargs['''stability_score_offset''']
           if "mask_threshold" in kwargs:
               __a						       =				kwargs['''mask_threshold''']
           if "stability_score_thresh" in kwargs:
               __a						       =				kwargs['''stability_score_thresh''']
           if "crops_nms_thresh" in kwargs:
               __a						       =				kwargs['''crops_nms_thresh''']
           if "output_rle_mask" in kwargs:
               __a						       =				kwargs['''output_rle_mask''']
           if "output_bboxes_mask" in kwargs:
               __a						       =				kwargs['''output_bboxes_mask''']
           return preprocess_kwargs, forward_params, postprocess_kwargs



       def __call__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE     ,		*__SCREAMING_SNAKE_CASE     ,		num_workers=__SCREAMING_SNAKE_CASE     ,		batch_size=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     int = 0     ,		__SCREAMING_SNAKE_CASE							:     float = 512 / 1_500     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 32     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 1     ,		):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor.size['''longest_edge''']
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.generate_crop_boxes(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')

           with self.device_placement():
               if self.framework == "pt":
                   __a						       =				self.get_inference_context()
                   with inference_context():
                       __a						       =				self._ensure_tensor_on_device(__SCREAMING_SNAKE_CASE     ,		device=self.device)
                       __a						       =				self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
                       __a						       =				image_embeddings

           __a						       =				grid_points.shape[1]
           __a						       =				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     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				grid_points[:, i : i + points_per_batch, :, :]
               __a						       =				input_labels[:, i : i + points_per_batch]
               __a						       =				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							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=0.88     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.95     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		__SCREAMING_SNAKE_CASE							:     int=1     ,		):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''input_boxes''')
           __a						       =				model_inputs.pop('''is_last''')
           __a						       =				model_inputs.pop('''original_sizes''').tolist()
           __a						       =				model_inputs.pop('''reshaped_input_sizes''').tolist()

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE)

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





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Dict=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     int=0.7     ,		):


           '''simple docstring'''

           __a						       =				[]
           __a						       =				[]
           __a						       =				[]
           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'''))

           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.post_process_for_mask_generation(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				defaultdict(__SCREAMING_SNAKE_CASE)
           for output in model_outputs:
               for k, v in output.items():
                   extra[k].append(__SCREAMING_SNAKE_CASE)

           __a						       =				{}
           if output_rle_mask:
               __a						       =				rle_mask

           if output_bboxes_mask:
               __a						       =				bounding_boxes

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





