


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__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=True    ,       lowercase__="pt"      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		{'''add_prefix_space''': True} if isinstance(lowercase__    ,       lowercase__      ) and not line.startswith(''' '''      ) else {}
						__SCREAMING_SNAKE_CASE						:     str					=		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__    ,       lowercase__    ,       lowercase__=None    ,       ):
						__SCREAMING_SNAKE_CASE						:     Union[str, 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        _lowercase      (			A__       ):






				'''simple docstring'''



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


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


				def __getitem__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str	)		->			Dict[str, torch.Tensor]:
										__SCREAMING_SNAKE_CASE						:     Any					=		index + 1  # linecache starts at 1
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.prefix + linecache.getline(str(self.src_file	)			,			lowerCAmelCase__	).rstrip('''\n'''	)
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer			,			lowerCAmelCase__	) else self.tokenizer
										)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.tokenizer.generator if isinstance(self.tokenizer			,			lowerCAmelCase__	) else self.tokenizer

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		encode_line(lowerCAmelCase__			,			lowerCAmelCase__			,			self.max_source_length			,			'''right'''	)
										__SCREAMING_SNAKE_CASE						:     str					=		encode_line(lowerCAmelCase__			,			lowerCAmelCase__			,			self.max_target_length			,			'''right'''	)

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


				@staticmethod
				def 		__magic_name__(       lowerCAmelCase__			:List[Any]	)		->			Optional[int]:
										return [len(lowerCAmelCase__	) for x in Path(lowerCAmelCase__	).open().readlines()]



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




__lowerCAmelCase	: Optional[int]  				=getLogger(__name__)


def        _UpperCamelCase							(      lowercase__      ):
						return list(itertools.chain.from_iterable(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		get_git_info()
						save_json(lowercase__    ,       os.path.join(lowercase__    ,       '''git_log.json'''      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=4    ,       **lowercase__      ):
						with open(lowercase__    ,       '''w'''      ) as f:
												json.dump(lowercase__    ,       lowercase__    ,       indent=lowercase__    ,       **lowercase__      )


def        _UpperCamelCase							(      lowercase__      ):
						with open(lowercase__      ) as f:
												return json.load(lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		git.Repo(search_parent_directories=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[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__    ,       lowercase__      ):
						return list(map(lowercase__    ,       lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						with open(lowercase__    ,       '''wb'''      ) as f:
												return pickle.dump(lowercase__    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__      ):

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

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

						def remove_punc(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     int					=		set(string.punctuation      )
												return "".join(ch for ch in text if ch not in exclude      )

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

						return white_space_fix(remove_articles(remove_punc(lower(lowercase__      )      )      )      )


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return normalize_answer(lowercase__      ) == normalize_answer(lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						assert len(lowercase__      ) == len(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		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__      ):
						return model_prefix.startswith('''rag'''      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						__SCREAMING_SNAKE_CASE						:     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
																		__SCREAMING_SNAKE_CASE						:     int					=		p if hasattr(lowercase__    ,       lowercase__      ) else equivalent_param[p]
																		setattr(lowercase__    ,       lowercase__    ,       getattr(lowercase__    ,       lowercase__      )      )
																		delattr(lowercase__    ,       lowercase__      )
						return hparams, config




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

										__SCREAMING_SNAKE_CASE						:     Tuple					=		parent
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		num_channels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_frames
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		min_resolution
										__SCREAMING_SNAKE_CASE						:     Any					=		max_resolution
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		do_resize
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		do_normalize
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_mean
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_std
										__SCREAMING_SNAKE_CASE						:     str					=		crop_size



				def 		__magic_name__(       self			:Tuple	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_center_crop'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 18}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 18, '''width''': 18}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.image_processing_class.from_dict(self.image_processor_dict			,			size=42			,			crop_size=84	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 42}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 84, '''width''': 84}	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)


				def 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			numpify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			np.ndarray	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Any					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)



				def 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			torchify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			torch.Tensor	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(video_inputs[0]			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(lowerCAmelCase__			,			return_tensors='''pt'''	).pixel_values
										self.assertEqual(
										    encoded_videos.shape			,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )			,			)
