



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    :		List[Any]  =logging.get_logger(__name__)

__snake_case    :		int  ={
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vivit"""



		def __init__(self		,__lowerCamelCase=2_24		,__lowerCamelCase=32		,__lowerCamelCase=[2, 16, 16]		,__lowerCamelCase=3		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu_fast"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					hidden_size
						lowerCAmelCase__					:    Tuple												=					num_hidden_layers
						lowerCAmelCase__					:    str												=					num_attention_heads
						lowerCAmelCase__					:    str												=					intermediate_size
						lowerCAmelCase__					:    str												=					hidden_act
						lowerCAmelCase__					:    Any												=					hidden_dropout_prob
						lowerCAmelCase__					:    Optional[int]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    Optional[int]												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps

						lowerCAmelCase__					:    Any												=					image_size
						lowerCAmelCase__					:    Tuple												=					num_frames
						lowerCAmelCase__					:    Optional[int]												=					tubelet_size
						lowerCAmelCase__					:    Any												=					num_channels
						lowerCAmelCase__					:    List[str]												=					qkv_bias

						super().__init__(**__lowerCamelCase			)






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__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		Any=True	,lowerCamelCase_		:		Tuple="pt"):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Any=None	,):





				'''simple docstring'''

				lowerCAmelCase__					:    List[Any]												=					input_ids.ne(lowerCamelCase_).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		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase="train"		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=""		,)					->      List[str]:

						"""simple docstring"""






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



		def __len__(self			)					->      str:

						"""simple docstring"""






						return len(self.src_lens			)



		def __getitem__(self		,__lowerCamelCase			)					->      Dict[str, torch.Tensor]:

						"""simple docstring"""






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

						lowerCAmelCase__					:    Union[str, Any]												=					encode_line(__lowerCamelCase		,__lowerCamelCase		,self.max_source_length		,'''right'''			)
						lowerCAmelCase__					:    Any												=					encode_line(__lowerCamelCase		,__lowerCamelCase		,self.max_target_length		,'''right'''			)

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



		@staticmethod
		def 		lowerCAmelCase__				(__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Dict[str, torch.Tensor]:

						"""simple docstring"""






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


__snake_case    :		Any  =getLogger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[List]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					get_git_info()
				save_json(lowerCamelCase_	,os.path.join(lowerCamelCase_	,'''git_log.json'''))






def      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Tuple=4	,**lowerCamelCase_		:		List[str]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Tuple):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					git.Repo(search_parent_directories=lowerCamelCase_)
				lowerCAmelCase__					:    List[Any]												=					{
				    '''repo_id''': str(lowerCamelCase_),
				    '''repo_sha''': str(repo.head.object.hexsha),
				    '''repo_branch''': str(repo.active_branch),
				    '''hostname''': str(socket.gethostname()),
				}
				return repo_infos






def      lowerCAmelCase__		(   lowerCamelCase_		:		Callable	,lowerCamelCase_		:		Iterable):





				'''simple docstring'''

				return list(map(lowerCamelCase_	,lowerCamelCase_))






def      lowerCAmelCase__		(   lowerCamelCase_		:		Any	,lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''simple docstring'''

				def remove_articles(lowerCamelCase_		:		List[str]):
								return re.sub(r'''\b(a|an|the)\b'''	,''' '''	,lowerCamelCase_)

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_))))






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Any):





				'''simple docstring'''

				return normalize_answer(lowerCamelCase_) == normalize_answer(lowerCamelCase_)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		List[str]):





				'''simple docstring'''

				assert len(lowerCamelCase_) == len(lowerCamelCase_)
				lowerCAmelCase__					:    List[str]												=					0
				for hypo, pred in zip(lowerCamelCase_	,lowerCamelCase_):
								em += exact_match_score(lowerCamelCase_	,lowerCamelCase_)
				if len(lowerCamelCase_) > 0:
								em /= len(lowerCamelCase_)
				return {"em": em}






def      lowerCAmelCase__		(   lowerCamelCase_		:		int):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[str]	,lowerCamelCase_		:		Dict	,lowerCamelCase_		:		str):





				'''simple docstring'''

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

