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     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Optional[int]						,__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Union[str, Any]=True						,__lowercase						:			Tuple="pt"     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			str						,__lowercase						:			Tuple=None						,):
		'''simple docstring'''




		A_		:    Any         =				input_ids.ne(__lowercase     ).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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase="train"		,     lowercase=None		,     lowercase=None		,     lowercase=None		,     lowercase=""		,     ):



						"""simple docstring"""





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



						"""simple docstring"""





						return len(self.src_lens )
				def __getitem__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				index + 1  # linecache starts at 1
						A_		:    Dict         =				self.prefix + linecache.getline(str(self.src_file )		,     lowercase ).rstrip('\n' )
						A_		:    Tuple         =				linecache.getline(str(self.tgt_file )		,     lowercase ).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		,     lowercase ):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

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

						A_		:    List[Any]         =				encode_line(lowercase		,     lowercase		,     self.max_source_length		,     'right' )
						A_		:    Dict         =				encode_line(lowercase		,     lowercase		,     self.max_target_length		,     'right' )

						A_		:    List[Any]         =				source_inputs['input_ids'].squeeze()
						A_		:    Union[str, Any]         =				target_inputs['input_ids'].squeeze()
						A_		:    Dict         =				source_inputs['attention_mask'].squeeze()
						return {
						    "input_ids": source_ids,
						    "attention_mask": src_mask,
						    "decoder_input_ids": target_ids,
						}
				@staticmethod
				def   lowerCAmelCase_       (						lowercase ):



						"""simple docstring"""





						return [len(lowercase ) for x in Path(lowercase ).open().readlines()]







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





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

_UpperCAmelCase            =		getLogger(__name__)




def     UpperCamelCase						(	__lowercase						:			List[List]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Optional[int]         =				get_git_info()
		save_json(__lowercase						,os.path.join(__lowercase						,'git_log.json'     )     )




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			str=4						,**__lowercase						:			str     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Tuple     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				git.Repo(search_parent_directories=__lowercase     )
		A_		:    Union[str, Any]         =				{
		    'repo_id': str(__lowercase     ),
		    'repo_sha': str(repo.head.object.hexsha     ),
		    'repo_branch': str(repo.active_branch     ),
		    'hostname': str(socket.gethostname()     ),
		}
		return repo_infos




def     UpperCamelCase						(	__lowercase						:			Callable						,__lowercase						:			Iterable     ):
		'''simple docstring'''




		return list(map(__lowercase						,__lowercase     )     )




def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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

		def white_space_fix(__lowercase						:			Any     ):
				return " ".join(text.split()     )

		def remove_punc(__lowercase						:			List[str]     ):
				A_		:    Tuple         =				set(string.punctuation     )
				return "".join(ch for ch in text if ch not in exclude     )

		def lower(__lowercase						:			List[Any]     ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(__lowercase     )     )     )     )




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    int         =				normalize_answer(__lowercase     ).split()
		A_		:    List[Any]         =				normalize_answer(__lowercase     ).split()
		A_		:    List[str]         =				Counter(__lowercase     ) & Counter(__lowercase     )
		A_		:    List[Any]         =				sum(common.values()     )
		if num_same == 0:
				return 0
		A_		:    Any         =				1.0 * num_same / len(__lowercase     )
		A_		:    Tuple         =				1.0 * num_same / len(__lowercase     )
		A_		:    Any         =				(2 * precision * recall) / (precision + recall)
		return fa




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			int     ):
		'''simple docstring'''




		return normalize_answer(__lowercase     ) == normalize_answer(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			List[str]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			str     ):
		'''simple docstring'''




		return model_prefix.startswith('rag'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Any						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




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







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase            =		{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''mask2former'''
				lowerCamelCase_    = ['''swin''']
				lowerCamelCase_    = {'''hidden_size''': '''hidden_dim'''}
				def __init__(						self		,     lowercase = None		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 2_5_6		,     lowercase = 1_0_2_4		,     lowercase = "relu"		,     lowercase = 6		,     lowercase = 1_0		,     lowercase = 8		,     lowercase = 0.0		,     lowercase = 2_0_4_8		,     lowercase = False		,     lowercase = False		,     lowercase = 4		,     lowercase = 2_5_5		,     lowercase = 1_0_0		,     lowercase = 0.1		,     lowercase = 2.0		,     lowercase = 5.0		,     lowercase = 5.0		,     lowercase = 1_2_5_4_4		,     lowercase = 3.0		,     lowercase = 0.75		,     lowercase = 0.02		,     lowercase = 1.0		,     lowercase = True		,     lowercase = [4, 8, 1_6, 3_2]		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' )
								A_		:    List[str]         =				CONFIG_MAPPING['swin'](
								    image_size=2_2_4		,     in_channels=3		,     patch_size=4		,     embed_dim=9_6		,     depths=[2, 2, 1_8, 2]		,     num_heads=[3, 6, 1_2, 2_4]		,     window_size=7		,     drop_path_rate=0.3		,     use_absolute_embeddings=lowercase		,     out_features=['stage1', 'stage2', 'stage3', 'stage4']		,     )

						if isinstance(lowercase		,     lowercase ):
								A_		:    str         =				backbone_config.pop('model_type' )
								A_		:    List[str]         =				CONFIG_MAPPING[backbone_model_type]
								A_		:    Tuple         =				config_class.from_dict(lowercase )

						# verify that the backbone is supported
						if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
								    F'''Supported model types: {','.join(self.backbones_supported )}''' )

						A_		:    List[Any]         =				backbone_config
						A_		:    Optional[Any]         =				feature_size
						A_		:    int         =				mask_feature_size
						A_		:    Tuple         =				hidden_dim
						A_		:    Dict         =				encoder_feedforward_dim
						A_		:    int         =				activation_function
						A_		:    str         =				encoder_layers
						A_		:    Tuple         =				decoder_layers
						A_		:    Tuple         =				num_attention_heads
						A_		:    str         =				dropout
						A_		:    List[str]         =				dim_feedforward
						A_		:    List[str]         =				pre_norm
						A_		:    Tuple         =				enforce_input_projection
						A_		:    Dict         =				common_stride
						A_		:    Union[str, Any]         =				ignore_value
						A_		:    List[Any]         =				num_queries
						A_		:    List[Any]         =				no_object_weight
						A_		:    int         =				class_weight
						A_		:    int         =				mask_weight
						A_		:    Optional[Any]         =				dice_weight
						A_		:    int         =				train_num_points
						A_		:    Optional[int]         =				oversample_ratio
						A_		:    Tuple         =				importance_sample_ratio
						A_		:    Union[str, Any]         =				init_std
						A_		:    List[Any]         =				init_xavier_std
						A_		:    Optional[Any]         =				use_auxiliary_loss
						A_		:    Dict         =				feature_strides
						A_		:    List[Any]         =				output_auxiliary_logits
						A_		:    Optional[int]         =				decoder_layers

						super().__init__(**lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     **lowercase ):



						"""simple docstring"""





						return cls(
						    backbone_config=lowercase		,     **lowercase		,     )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				copy.deepcopy(self.__dict__ )
						A_		:    Optional[int]         =				self.backbone_config.to_dict()
						A_		:    Dict         =				self.__class__.model_type
						return output






