




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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=['''pixel_values''']

			def __init__( self			:   int       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = PILImageResampling.BILINEAR       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Union[int, float] = 1 / 255       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = True       ,						**lowerCamelCase_			:   Tuple       ,						)						->   None:
								super().__init__(**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							size if size is not None else {'''shortest_edge''': 224}
								__magic_name__							:	List[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
								__magic_name__							:	Dict    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

								__magic_name__							:	Optional[Any]    							=							do_resize
								__magic_name__							:	List[str]    							=							size
								__magic_name__							:	Union[str, Any]    							=							resample
								__magic_name__							:	List[Any]    							=							do_rescale
								__magic_name__							:	Union[str, Any]    							=							rescale_factor
								__magic_name__							:	List[str]    							=							do_center_crop
								__magic_name__							:	Optional[int]    							=							crop_size
								__magic_name__							:	List[str]    							=							do_flip_channel_order

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   PILImageResampling = PIL.Image.BILINEAR       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   np.ndarray:
								__magic_name__							:	List[Any]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								if "shortest_edge" not in size:
													raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''  )
								__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Dict[str, int]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   Dict       ,						)						->   np.ndarray:
								__magic_name__							:	Union[str, Any]    							=							get_size_dict(lowerCamelCase_  )
								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()}'''  )
								return center_crop(lowerCamelCase_       ,						size=(size['''height'''], size['''width'''])       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Union[int, float]       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None       ,						**lowerCamelCase_			:   str       ,						)						->   Tuple:
								return rescale(lowerCamelCase_       ,						scale=lowerCamelCase_       ,						data_format=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   np.ndarray       ,						lowerCamelCase_			:   Optional[Union[str, ChannelDimension]] = None  )						->   np.ndarray:
								return flip_channel_order(lowerCamelCase_       ,						data_format=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   ImageInput       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   PILImageResampling = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   float = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Dict[str, int] = None       ,						lowerCamelCase_			:   bool = None       ,						lowerCamelCase_			:   Optional[Union[str, TensorType]] = None       ,						lowerCamelCase_			:   ChannelDimension = ChannelDimension.FIRST       ,						**lowerCamelCase_			:   List[Any]       ,						)						->   PIL.Image.Image:
								__magic_name__							:	Tuple    							=							do_resize if do_resize is not None else self.do_resize
								__magic_name__							:	List[str]    							=							resample if resample is not None else self.resample
								__magic_name__							:	int    							=							do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__							:	Optional[int]    							=							rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__							:	str    							=							do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__							:	Tuple    							=							(
								    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
								)

								__magic_name__							:	Tuple    							=							size if size is not None else self.size
								__magic_name__							:	Optional[int]    							=							get_size_dict(lowerCamelCase_       ,						default_to_square=lowerCamelCase_  )
								__magic_name__							:	int    							=							crop_size if crop_size is not None else self.crop_size
								__magic_name__							:	Optional[Any]    							=							get_size_dict(lowerCamelCase_       ,						param_name='''crop_size'''  )

								__magic_name__							:	Tuple    							=							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_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

								if do_center_crop and crop_size is None:
													raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

								# All transformations expect numpy arrays.
								__magic_name__							:	Tuple    							=							[to_numpy_array(lowerCamelCase_  ) for image in images]

								if do_resize:
													__magic_name__							:	int    							=							[self.resize(image=lowerCamelCase_       ,						size=lowerCamelCase_       ,						resample=lowerCamelCase_  ) for image in images]

								if do_center_crop:
													__magic_name__							:	Optional[Any]    							=							[self.center_crop(image=lowerCamelCase_       ,						size=lowerCamelCase_  ) for image in images]

								if do_rescale:
													__magic_name__							:	Optional[int]    							=							[self.rescale(image=lowerCamelCase_       ,						scale=lowerCamelCase_  ) for image in images]

								# the pretrained checkpoints assume images are BGR, not RGB
								if do_flip_channel_order:
													__magic_name__							:	Any    							=							[self.flip_channel_order(image=lowerCamelCase_  ) for image in images]

								__magic_name__							:	Dict    							=							[to_channel_dimension_format(lowerCamelCase_       ,						lowerCamelCase_  ) for image in images]

								__magic_name__							:	List[Any]    							=							{'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase_       ,						tensor_type=lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   List[Tuple] = None  )						->   Union[str, Any]:
								__magic_name__							:	List[str]    							=							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_  ):
																		__magic_name__							:	List[Any]    							=							target_sizes.numpy()

													__magic_name__							:	Optional[int]    							=							[]

													for idx in range(len(lowerCamelCase_  )  ):
																		__magic_name__							:	Union[str, Any]    							=							torch.nn.functional.interpolate(
																		    logits[idx].unsqueeze(dim=0  )       ,						size=target_sizes[idx]       ,						mode='''bilinear'''       ,						align_corners=lowerCamelCase_  )
																		__magic_name__							:	Optional[int]    							=							resized_logits[0].argmax(dim=0  )
																		semantic_segmentation.append(lowerCamelCase_  )
								else:
													__magic_name__							:	Dict    							=							logits.argmax(dim=1  )
													__magic_name__							:	Optional[Any]    							=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

								return semantic_segmentation









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__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							input_ids.ne(__A				).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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

			def __len__( self			:   Optional[int]  )						->   List[Any]:
								return len(self.src_lens  )

			def __getitem__( self			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	Optional[int]    							=							index + 1  # linecache starts at 1
								__magic_name__							:	Optional[int]    							=							self.prefix + linecache.getline(str(self.src_file  )       ,						lowerCamelCase_  ).rstrip('''\n'''  )
								__magic_name__							:	Dict    							=							linecache.getline(str(self.tgt_file  )       ,						lowerCamelCase_  ).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       ,						lowerCamelCase_  ):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__magic_name__							:	int    							=							(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer
								)
								__magic_name__							:	Optional[Any]    							=							self.tokenizer.generator if isinstance(self.tokenizer       ,						lowerCamelCase_  ) else self.tokenizer

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

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

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[Any]  )						->   Dict[str, torch.Tensor]:
								__magic_name__							:	str    							=							torch.stack([x['''input_ids'''] for x in batch]  )
								__magic_name__							:	Any    							=							torch.stack([x['''attention_mask'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							torch.stack([x['''decoder_input_ids'''] for x in batch]  )
								__magic_name__							:	Tuple    							=							(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Optional[Any]    							=							(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,						lowerCamelCase_  )
								    else self.tokenizer.pad_token_id
								)
								__magic_name__							:	Dict    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							,    __magic_name__							:	Optional[int]    							=							trim_batch(lowerCamelCase_       ,						lowerCamelCase_       ,						attention_mask=lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					def remove_articles(__A:	Any				):
										return re.sub(r'''\b(a|an|the)\b'''							,''' '''							,__A				)

					def white_space_fix(__A:	Optional[int]				):
										return " ".join(text.split()				)

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

					def lower(__A:	Optional[int]				):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Optional[int]    							=							normalize_answer(__A				).split()
					__magic_name__							:	Dict    							=							Counter(__A				) & Counter(__A				)
					__magic_name__							:	Tuple    							=							sum(common.values()				)
					if num_same == 0:
										return 0
					__magic_name__							:	int    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	Tuple    							=							1.0 * num_same / len(__A				)
					__magic_name__							:	str    							=							(2 * precision * recall) / (precision + recall)
					return fa







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							0
					for hypo, pred in zip(__A							,__A				):
										em += exact_match_score(__A							,__A				)
					if len(__A				) > 0:
										em /= len(__A				)
					return {"em": em}







def 		lowercase__   (		__A:	int				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






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



