

"""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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=True				,     __UpperCamelCase="pt"   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       {"""add_prefix_space""": True} if isinstance(__UpperCamelCase				,     __UpperCamelCase   ) and not line.startswith(""" """   ) else {}
 UpperCAmelCase__       :	List[Any]        =       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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=None				,     ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[str]        =       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   __lowercase      (     __lowerCamelCase       ):

  def __init__(	self		:      Optional[int]       ,A		:      Optional[int]       ,A		:      Any       ,A		:      Optional[int]       ,A		:      List[Any]       ,A		:      Optional[int]="train"       ,A		:      Any=None       ,A		:      Optional[int]=None       ,A		:      Union[str, Any]=None       ,A		:      Tuple=""       ,):






   '''simple docstring'''
   super().__init__()
   UpperCAmelCase__       :	Union[str, Any]        =       Path(A     ).joinpath(type_path + """.source"""     )
   UpperCAmelCase__       :	Union[str, Any]        =       Path(A     ).joinpath(type_path + """.target"""     )
   UpperCAmelCase__       :	int        =       self.get_char_lens(self.src_file     )
   UpperCAmelCase__       :	str        =       max_source_length
   UpperCAmelCase__       :	Any        =       max_target_length
   assert min(self.src_lens     ) > 0, f"found empty line in {self.src_file}"
   UpperCAmelCase__       :	Optional[int]        =       tokenizer
   UpperCAmelCase__       :	int        =       prefix
   if n_obs is not None:
    UpperCAmelCase__       :	Union[str, Any]        =       self.src_lens[:n_obs]
   UpperCAmelCase__       :	List[str]        =       src_lang
   UpperCAmelCase__       :	Optional[int]        =       tgt_lang

  def __len__(	self		:      List[str]     ):






   '''simple docstring'''
   return len(self.src_lens     )

  def __getitem__(	self		:      List[Any]       ,A		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       index + 1  # linecache starts at 1
   UpperCAmelCase__       :	List[str]        =       self.prefix + linecache.getline(str(self.src_file     )       ,A     ).rstrip("""\n"""     )
   UpperCAmelCase__       :	List[str]        =       linecache.getline(str(self.tgt_file     )       ,A     ).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       ,A     ):
    source_line += self.tokenizer.eos_token
    tgt_line += self.tokenizer.eos_token

   # Pad source and target to the right
   UpperCAmelCase__       :	int        =       (
       self.tokenizer.question_encoder if isinstance(self.tokenizer       ,A     ) else self.tokenizer
   )
   UpperCAmelCase__       :	int        =       self.tokenizer.generator if isinstance(self.tokenizer       ,A     ) else self.tokenizer

   UpperCAmelCase__       :	Optional[int]        =       encode_line(A       ,A       ,self.max_source_length       ,"""right"""     )
   UpperCAmelCase__       :	str        =       encode_line(A       ,A       ,self.max_target_length       ,"""right"""     )

   UpperCAmelCase__       :	Optional[Any]        =       source_inputs["""input_ids"""].squeeze()
   UpperCAmelCase__       :	Dict        =       target_inputs["""input_ids"""].squeeze()
   UpperCAmelCase__       :	int        =       source_inputs["""attention_mask"""].squeeze()
   return {
       "input_ids": source_ids,
       "attention_mask": src_mask,
       "decoder_input_ids": target_ids,
   }

  @staticmethod
  def  __lowercase							(	A		:      List[str]     ):






   '''simple docstring'''
   return [len(A     ) for x in Path(A     ).open().readlines()]




  def  __lowercase							(	self		:      Optional[Any]       ,A		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       torch.stack([x["""input_ids"""] for x in batch]     )
   UpperCAmelCase__       :	Optional[Any]        =       torch.stack([x["""attention_mask"""] for x in batch]     )
   UpperCAmelCase__       :	str        =       torch.stack([x["""decoder_input_ids"""] for x in batch]     )
   UpperCAmelCase__       :	Optional[int]        =       (
       self.tokenizer.generator.pad_token_id
       if isinstance(self.tokenizer       ,A     )
       else self.tokenizer.pad_token_id
   )
   UpperCAmelCase__       :	List[str]        =       (
       self.tokenizer.question_encoder.pad_token_id
       if isinstance(self.tokenizer       ,A     )
       else self.tokenizer.pad_token_id
   )
   UpperCAmelCase__       :	Any        =       trim_batch(A       ,A     )
   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[str]        =       trim_batch(A       ,A       ,attention_mask=A     )
   UpperCAmelCase__       :	Any        =       {
       """input_ids""": source_ids,
       """attention_mask""": source_mask,
       """decoder_input_ids""": y,
   }
   return batch







__UpperCAmelCase			=					getLogger(__name__)
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return list(itertools.chain.from_iterable(__UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	int        =       get_git_info()
 save_json(__UpperCamelCase				,     os.path.join(__UpperCamelCase				,     """git_log.json"""   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=4				,     **__UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """w"""   ) as f:
  json.dump(__UpperCamelCase				,     __UpperCamelCase				,     indent=__UpperCamelCase				,     **__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase   ) as f:
  return json.load(__UpperCamelCase   )
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       git.Repo(search_parent_directories=__UpperCamelCase   )
 UpperCAmelCase__       :	Optional[Any]        =       {
     """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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return list(map(__UpperCamelCase				,     __UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 with open(__UpperCamelCase				,     """wb"""   ) as f:
  return pickle.dump(__UpperCamelCase				,     __UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 def remove_articles(__UpperCamelCase   ):
  return re.sub(r"""\b(a|an|the)\b"""				,     """ """				,     __UpperCamelCase   )

 def white_space_fix(__UpperCamelCase   ):
  return " ".join(text.split()   )

 def remove_punc(__UpperCamelCase   ):
  UpperCAmelCase__       :	Dict        =       set(string.punctuation   )
  return "".join(ch for ch in text if ch not in exclude   )

 def lower(__UpperCamelCase   ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase   )   )   )   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Tuple        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	str        =       normalize_answer(__UpperCamelCase   ).split()
 UpperCAmelCase__       :	str        =       Counter(__UpperCamelCase   ) & Counter(__UpperCamelCase   )
 UpperCAmelCase__       :	int        =       sum(common.values()   )
 if num_same == 0:
  return 0
 UpperCAmelCase__       :	Union[str, Any]        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	str        =       1.0 * num_same / len(__UpperCamelCase   )
 UpperCAmelCase__       :	Any        =       (2 * precision * recall) / (precision + recall)
 return fa
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return normalize_answer(__UpperCamelCase   ) == normalize_answer(__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 assert len(__UpperCamelCase   ) == len(__UpperCamelCase   )
 UpperCAmelCase__       :	Any        =       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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return model_prefix.startswith("""rag"""   )




def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Any        =       {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCAmelCase__       :	Any        =       """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__       :	Union[str, Any]        =       p if hasattr(__UpperCamelCase				,     __UpperCamelCase   ) else equivalent_param[p]
   setattr(__UpperCamelCase				,     __UpperCamelCase				,     getattr(__UpperCamelCase				,     __UpperCamelCase   )   )
   delattr(__UpperCamelCase				,     __UpperCamelCase   )
 return hparams, config











from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
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_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
							__lowerCamelCase									=       get_resize_output_image_size(lowerCamelCase__     ,		size=size['shortest_edge']     ,		default_to_square=lowerCamelCase__				)
							return resize(lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
							return center_crop(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''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(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__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									=       make_list_of_images(lowerCamelCase__				)

							if not valid_images(lowerCamelCase__				):
											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_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop 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('Image mean and std must be specified if do_normalize is True.'				)

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

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

							if do_center_crop:
											__lowerCamelCase									=       [self.center_crop(image=lowerCamelCase__     ,		size=lowerCamelCase__				) for image in images]

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

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

							__lowerCamelCase									=       [to_channel_dimension_format(lowerCamelCase__     ,		lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(
															    'Make sure that you pass in as many target sizes as the batch dimension of the logits'				)

											if is_torch_tensor(lowerCamelCase__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

											for idx in range(len(lowerCamelCase__				)				):
															__lowerCamelCase									=       torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0				)     ,		size=target_sizes[idx]     ,		mode='bilinear'     ,		align_corners=lowerCamelCase__				)
															__lowerCamelCase									=       resized_logits[0].argmax(dim=0				)
															semantic_segmentation.append(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       logits.argmax(dim=1				)
											__lowerCamelCase									=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

							return semantic_segmentation


