
'''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 	_snake_case						(      A_       :    Optional[Any]     ,		A_       :    str     ,		A_       :    str     ,		A_       :    Any     ,		A_       :    Union[str, Any]=True     ,		A_       :    Union[str, Any]="pt"   ):
		"""simple docstring"""







		a_      :	List[Any]      =							{"""add_prefix_space""": True} if isinstance(A_     ,		A_   ) and not line.startswith(""" """   ) else {}
		a_      :	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 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Optional[Any]     ,		A_       :    Union[str, Any]=None     ,		):
		"""simple docstring"""







		a_      :	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  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

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

							'''simple docstring'''

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


					def __len__(      self ):

							'''simple docstring'''

							return len(self.src_lens )


					def __getitem__(      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

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

							a_      :	List[Any]      =							encode_line(lowerCAmelCase_							,		lowerCAmelCase_							,		self.max_source_length							,		"""right""" )
							a_      :	Optional[Any]      =							encode_line(lowerCAmelCase_							,		lowerCAmelCase_							,		self.max_target_length							,		"""right""" )

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


					@staticmethod
					def 							_lowerCAmelCase  (      lowerCAmelCase_ ):

							'''simple docstring'''

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






					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

							'''simple docstring'''

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







__snake_case:  Union[str, Any]								=			getLogger(__name__)






def 	_snake_case						(      A_       :    List[List]   ):
		"""simple docstring"""







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






def 	_snake_case						(      A_       :    str   ):
		"""simple docstring"""







		a_      :	List[str]      =							get_git_info()
		save_json(A_     ,		os.path.join(A_     ,		"""git_log.json"""   )   )






def 	_snake_case						(      A_       :    List[Any]     ,		A_       :    Union[str, Any]     ,		A_       :    int=4     ,		**A_       :    Any   ):
		"""simple docstring"""







		with open(A_     ,		"""w"""   ) as f:
				json.dump(A_     ,		A_     ,		indent=A_     ,		**A_   )






def 	_snake_case						(      A_       :    Optional[Any]   ):
		"""simple docstring"""







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






def 	_snake_case						(      ):
		"""simple docstring"""







		a_      :	str      =							git.Repo(search_parent_directories=A_   )
		a_      :	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 	_snake_case						(      A_       :    Callable     ,		A_       :    Iterable   ):
		"""simple docstring"""







		return list(map(A_     ,		A_   )   )






def 	_snake_case						(      A_       :    str     ,		A_       :    str   ):
		"""simple docstring"""







		with open(A_     ,		"""wb"""   ) as f:
				return pickle.dump(A_     ,		A_   )






def 	_snake_case						(      A_       :    Optional[Any]   ):
		"""simple docstring"""







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

		def white_space_fix(A_       :    Dict   ):
				return " ".join(text.split()   )

		def remove_punc(A_       :    Optional[Any]   ):
				a_      :	Optional[Any]      =							set(string.punctuation   )
				return "".join(ch for ch in text if ch not in exclude   )

		def lower(A_       :    Optional[Any]   ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(A_   )   )   )   )






def 	_snake_case						(      A_       :    str     ,		A_       :    Any   ):
		"""simple docstring"""







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






def 	_snake_case						(      A_       :    Tuple     ,		A_       :    int   ):
		"""simple docstring"""







		return normalize_answer(A_   ) == normalize_answer(A_   )






def 	_snake_case						(      A_       :    List[str]     ,		A_       :    List[str]   ):
		"""simple docstring"""







		assert len(A_   ) == len(A_   )
		a_      :	Tuple      =							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 	_snake_case						(      A_       :    str   ):
		"""simple docstring"""







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






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    List[str]     ,		A_       :    Optional[int]   ):
		"""simple docstring"""







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




'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


