






"""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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=True   ,   snake_case__="pt"					):
	A_		:						Union[str, Any]	    =   {"""add_prefix_space""": True} if isinstance(snake_case__   ,   snake_case__					) and not line.startswith(""" """					) else {}
	A_		:						List[Any]	    =   padding_side
	return tokenizer(
	    [line]   ,   max_length=snake_case__   ,   padding="""max_length""" if pad_to_max_length else None   ,   truncation=snake_case__   ,   return_tensors=snake_case__   ,   add_special_tokens=snake_case__   ,   **snake_case__   ,   )






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=None   ,   ):
	A_		:						Dict	    =   input_ids.ne(snake_case__					).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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




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





	def __len__(self							):
		return len(self.src_lens							)





	def __getitem__(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   index + 1  # linecache starts at 1
		A_		:						Any	    =   self.prefix + linecache.getline(str(self.src_file							)     ,     lowerCAmelCase_							).rstrip("""\n"""							)
		A_		:						List[str]	    =   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
		A_		:						Any	    =   (
		    self.tokenizer.question_encoder if isinstance(self.tokenizer     ,     lowerCAmelCase_							) else self.tokenizer
		)
		A_		:						Tuple	    =   self.tokenizer.generator if isinstance(self.tokenizer     ,     lowerCAmelCase_							) else self.tokenizer

		A_		:						Union[str, Any]	    =   encode_line(lowerCAmelCase_     ,     lowerCAmelCase_     ,     self.max_source_length     ,     """right"""							)
		A_		:						List[Any]	    =   encode_line(lowerCAmelCase_     ,     lowerCAmelCase_     ,     self.max_target_length     ,     """right"""							)

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





	@staticmethod
	def    lowerCamelCase(lowerCAmelCase_							):
		return [len(lowerCAmelCase_							) for x in Path(lowerCAmelCase_							).open().readlines()]





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






_lowerCAmelCase         =							getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	return list(itertools.chain.from_iterable(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						str	    =   get_git_info()
	save_json(snake_case__   ,   os.path.join(snake_case__   ,   """git_log.json"""					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=4   ,   **snake_case__					):
	with open(snake_case__   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=snake_case__   ,   **snake_case__					)






def 			__UpperCamelCase	(		snake_case__					):
	with open(snake_case__					) as f:
		return json.load(snake_case__					)






def 			__UpperCamelCase	(		):
	A_		:						int	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						Tuple	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	    """hostname""": str(socket.gethostname()					),
	}
	return repo_infos






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	return list(map(snake_case__   ,   snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	with open(snake_case__   ,   """wb"""					) as f:
		return pickle.dump(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__					):

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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__					)					)					)					)






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	return normalize_answer(snake_case__					) == normalize_answer(snake_case__					)






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






def 			__UpperCamelCase	(		snake_case__					):
	return model_prefix.startswith("""rag"""					)






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









"""simple docstring"""
import copy

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


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		if two_stage is True and with_box_refine is False:
			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
		# Hungarian matcher
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

