




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_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor


class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Union[str, Any]=13       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						lowerCamelCase_			:   Tuple=224       ,						lowerCamelCase_			:   Optional[Any]=30       ,						lowerCamelCase_			:   Tuple=400       ,						lowerCamelCase_			:   Tuple=True       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   str=True       ,						lowerCamelCase_			:   int=[0.5, 0.5, 0.5]       ,						lowerCamelCase_			:   Tuple=[0.5, 0.5, 0.5]       ,						)						->   Tuple:
								__magic_name__							:	List[Any]    							=							size if size is not None else {'''height''': 18, '''width''': 18}
								__magic_name__							:	Any    							=							parent
								__magic_name__							:	str    							=							batch_size
								__magic_name__							:	Tuple    							=							num_channels
								__magic_name__							:	str    							=							image_size
								__magic_name__							:	Dict    							=							min_resolution
								__magic_name__							:	str    							=							max_resolution
								__magic_name__							:	int    							=							do_resize
								__magic_name__							:	List[Any]    							=							size
								__magic_name__							:	Tuple    							=							do_normalize
								__magic_name__							:	Dict    							=							image_mean
								__magic_name__							:	str    							=							image_std


			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}






@require_torch
@require_vision
class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=ViTImageProcessor if is_vision_available() else None

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   List[Any]:
								__magic_name__							:	str    							=							EfficientFormerImageProcessorTester(self  )

			@property
			def 							UpperCAmelCase__       ( self			:   str  )						->   int:
								return self.image_proc_tester.prepare_image_processor_dict()

			def 							UpperCAmelCase__       ( self			:   str  )						->   int:
								__magic_name__							:	Dict    							=							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_       ,						'''size'''  )  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   List[str]:
								pass

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[Any]:
								# Initialize image_processor
								__magic_name__							:	List[Any]    							=							self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								__magic_name__							:	int    							=							prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase_  )
								for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_       ,						Image.Image  )

								# Test not batched input
								__magic_name__							:	Tuple    							=							image_processor(image_inputs[0]       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)

								# Test batched
								__magic_name__							:	Optional[Any]    							=							image_processor(lowerCamelCase_       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Union[str, Any]:
								# Initialize image_processor
								__magic_name__							:	List[str]    							=							self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								__magic_name__							:	str    							=							prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase_       ,						numpify=lowerCamelCase_  )
								for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_       ,						np.ndarray  )

								# Test not batched input
								__magic_name__							:	str    							=							image_processor(image_inputs[0]       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)

								# Test batched
								__magic_name__							:	List[Any]    							=							image_processor(lowerCamelCase_       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)


			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Union[str, Any]:
								# Initialize image_processor
								__magic_name__							:	str    							=							self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								__magic_name__							:	Tuple    							=							prepare_image_inputs(self.image_proc_tester       ,						equal_resolution=lowerCamelCase_       ,						torchify=lowerCamelCase_  )
								for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_       ,						torch.Tensor  )

								# Test not batched input
								__magic_name__							:	int    							=							image_processor(image_inputs[0]       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)

								# Test batched
								__magic_name__							:	List[Any]    							=							image_processor(lowerCamelCase_       ,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    )       ,						)









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 		lowercase__   (		__A:	str							,__A:	Optional[Any]							,__A:	List[Any]							,__A:	str							,__A:	List[str]=True							,__A:	List[Any]="pt"				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							{'''add_prefix_space''': True} if isinstance(__A							,__A				) and not line.startswith(''' '''				) else {}
					__magic_name__							:	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 		lowercase__   (		__A:	int							,__A:	int							,__A:	Optional[Any]=None							,):





					'''simple docstring'''






					__magic_name__							:	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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

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

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

								__magic_name__							:	Tuple    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_source_length       ,						'''right'''  )
								__magic_name__							:	int    							=							encode_line(lowerCamelCase_       ,						lowerCamelCase_       ,						self.max_target_length       ,						'''right'''  )

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

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   List[str]  )						->   Dict:
								return [len(lowerCamelCase_  ) for x in Path(lowerCamelCase_  ).open().readlines()]


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






__lowerCamelCase   :						Dict				=			getLogger(__name__)







def 		lowercase__   (		__A:	List[List]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							get_git_info()
					save_json(__A							,os.path.join(__A							,'''git_log.json'''				)				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Union[str, Any]							,__A:	Union[str, Any]=4							,**__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	int    							=							git.Repo(search_parent_directories=__A				)
					__magic_name__							:	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 		lowercase__   (		__A:	Callable							,__A:	Iterable				):





					'''simple docstring'''






					return list(map(__A							,__A				)				)







def 		lowercase__   (		__A:	int							,__A:	Dict				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






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

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

					def remove_punc(__A:	Any				):
										__magic_name__							:	Union[str, Any]    							=							set(string.punctuation				)
										return "".join(ch for ch in text if ch not in exclude				)

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

					return white_space_fix(remove_articles(remove_punc(lower(__A				)				)				)				)







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					return normalize_answer(__A				) == normalize_answer(__A				)







def 		lowercase__   (		__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					assert len(__A				) == len(__A				)
					__magic_name__							:	Optional[int]    							=							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 		lowercase__   (		__A:	int				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Optional[Any]							,__A:	List[str]							,__A:	Dict				):





					'''simple docstring'''






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



