



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor







class     __lowerCAmelCase					(    unittest.TestCase		):



			def __init__(		self     :						List[Any]				,	A     :						Tuple				,	A     :						int=7				,	A     :						str=3				,	A     :						List[Any]=30				,	A     :						str=4_00				,	A     :						Tuple=True				,	A     :						str=None				,	A     :						Tuple=True				,	A     :						List[str]=[0.5, 0.5, 0.5]				,	A     :						int=[0.5, 0.5, 0.5]				,	A     :						List[str]=True				,	A     :						List[str]=1 / 2_55				,	A     :						List[Any]=True				,	)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     min_resolution
						_UpperCAmelCase	 =     max_resolution
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean
						_UpperCAmelCase	 =     image_std
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_pad



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Any:



						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}



			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						int				,	A     :						int=False)     ->					Dict:



						"""simple docstring"""






						if not batched:
									_UpperCAmelCase	 =     image_inputs[0]
									if isinstance(A				,	Image.Image):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     image.size
									else:
												_UpperCAmelCase ,  _UpperCAmelCase	 =     image.shape[1], image.shape[2]
									if w < h:
												_UpperCAmelCase	 =     int(self.size['shortest_edge'] * h / w)
												_UpperCAmelCase	 =     self.size['shortest_edge']
									elif w > h:
												_UpperCAmelCase	 =     self.size['shortest_edge']
												_UpperCAmelCase	 =     int(self.size['shortest_edge'] * w / h)
									else:
												_UpperCAmelCase	 =     self.size['shortest_edge']
												_UpperCAmelCase	 =     self.size['shortest_edge']

						else:
									_UpperCAmelCase	 =     []
									for image in image_inputs:
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_expected_values([image])
												expected_values.append((expected_height, expected_width))
									_UpperCAmelCase	 =     max(A				,	key=lambda A: item[0])[0]
									_UpperCAmelCase	 =     max(A				,	key=lambda A: item[1])[1]

						return expected_height, expected_width







@require_torch
@require_vision
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  DeformableDetrImageProcessor if is_vision_available() else None



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     DeformableDetrImageProcessingTester(self)



			@property
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



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



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A				,	'image_mean'))
						self.assertTrue(hasattr(A				,	'image_std'))
						self.assertTrue(hasattr(A				,	'do_normalize'))
						self.assertTrue(hasattr(A				,	'do_resize'))
						self.assertTrue(hasattr(A				,	'do_rescale'))
						self.assertTrue(hasattr(A				,	'do_pad'))
						self.assertTrue(hasattr(A				,	'size'))



			def 					_lowerCamelCase (		self     :						str)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size				,	{'shortest_edge': 18, 'longest_edge': 13_33})
						self.assertEqual(image_processor.do_pad				,	A)

						_UpperCAmelCase	 =     self.image_processing_class.from_dict(
						    self.image_processor_dict				,	size=42				,	max_size=84				,	pad_and_return_pixel_mask=A)
						self.assertEqual(image_processor.size				,	{'shortest_edge': 42, 'longest_edge': 84})
						self.assertEqual(image_processor.do_pad				,	A)



			def 					_lowerCamelCase (		self     :						List[str])     ->					Dict:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						List[str])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A)
						for image in image_inputs:
									self.assertIsInstance(A				,	Image.Image)

						# Test not batched input
						_UpperCAmelCase	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A)

						self.assertEqual(
						    encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

						# Test batched
						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A				,	batched=A)

						_UpperCAmelCase	 =     image_processing(A				,	return_tensors='pt').pixel_values
						self.assertEqual(
						    encoded_images.shape				,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,	)



			def 					_lowerCamelCase (		self     :						List[str])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A				,	numpify=A)
						for image in image_inputs:
									self.assertIsInstance(A				,	np.ndarray)

						# Test not batched input
						_UpperCAmelCase	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A)

						self.assertEqual(
						    encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(A				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A				,	batched=A)

						self.assertEqual(
						    encoded_images.shape				,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,	)



			def 					_lowerCamelCase (		self     :						List[str])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=A				,	torchify=A)
						for image in image_inputs:
									self.assertIsInstance(A				,	torch.Tensor)

						# Test not batched input
						_UpperCAmelCase	 =     image_processing(image_inputs[0]				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A)

						self.assertEqual(
						    encoded_images.shape				,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(A				,	return_tensors='pt').pixel_values

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.image_processor_tester.get_expected_values(A				,	batched=A)

						self.assertEqual(
						    encoded_images.shape				,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )				,	)



			@slow
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,	'r') as f:
									_UpperCAmelCase	 =     json.loads(f.read())

						_UpperCAmelCase	 =     {'image_id': 3_97_69, 'annotations': target}

						# encode them
						_UpperCAmelCase	 =     DeformableDetrImageProcessor()
						_UpperCAmelCase	 =     image_processing(images=A				,	annotations=A				,	return_tensors='pt')

						# verify pixel values
						_UpperCAmelCase	 =     torch.Size([1, 3, 8_00, 10_66])
						self.assertEqual(encoding['pixel_values'].shape				,	A)

						_UpperCAmelCase	 =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,	A				,	atol=1E-4))

						# verify area
						_UpperCAmelCase	 =     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8])
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,	A))
						# verify boxes
						_UpperCAmelCase	 =     torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape				,	A)
						_UpperCAmelCase	 =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,	A				,	atol=1E-3))
						# verify image_id
						_UpperCAmelCase	 =     torch.tensor([3_97_69])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,	A))
						# verify is_crowd
						_UpperCAmelCase	 =     torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,	A))
						# verify class_labels
						_UpperCAmelCase	 =     torch.tensor([75, 75, 63, 65, 17, 17])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,	A))
						# verify orig_size
						_UpperCAmelCase	 =     torch.tensor([4_80, 6_40])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,	A))
						# verify size
						_UpperCAmelCase	 =     torch.tensor([8_00, 10_66])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,	A))



			@slow
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,	'r') as f:
									_UpperCAmelCase	 =     json.loads(f.read())

						_UpperCAmelCase	 =     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

						_UpperCAmelCase	 =     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

						# encode them
						_UpperCAmelCase	 =     DeformableDetrImageProcessor(format='coco_panoptic')
						_UpperCAmelCase	 =     image_processing(images=A				,	annotations=A				,	masks_path=A				,	return_tensors='pt')

						# verify pixel values
						_UpperCAmelCase	 =     torch.Size([1, 3, 8_00, 10_66])
						self.assertEqual(encoding['pixel_values'].shape				,	A)

						_UpperCAmelCase	 =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,	A				,	atol=1E-4))

						# verify area
						_UpperCAmelCase	 =     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7])
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,	A))
						# verify boxes
						_UpperCAmelCase	 =     torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape				,	A)
						_UpperCAmelCase	 =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,	A				,	atol=1E-3))
						# verify image_id
						_UpperCAmelCase	 =     torch.tensor([3_97_69])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,	A))
						# verify is_crowd
						_UpperCAmelCase	 =     torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,	A))
						# verify class_labels
						_UpperCAmelCase	 =     torch.tensor([17, 17, 63, 75, 75, 93])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,	A))
						# verify masks
						_UpperCAmelCase	 =     82_28_73
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,	A)
						# verify orig_size
						_UpperCAmelCase	 =     torch.tensor([4_80, 6_40])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,	A))
						# verify size
						_UpperCAmelCase	 =     torch.tensor([8_00, 10_66])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,	A))





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 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Dict=True							,     _UpperCAmelCase       :     Tuple="pt"					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {'add_prefix_space': True} if isinstance(_UpperCAmelCase							,     _UpperCAmelCase					) and not line.startswith(' '					) else {}
			_UpperCAmelCase	 =     padding_side
			return tokenizer(
			    [line]							,     max_length=_UpperCAmelCase							,     padding='max_length' if pad_to_max_length else None							,     truncation=_UpperCAmelCase							,     return_tensors=_UpperCAmelCase							,     add_special_tokens=_UpperCAmelCase							,     **_UpperCAmelCase							,     )





def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=None							,     )       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     input_ids.ne(_UpperCAmelCase					).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					(    A		):



			def __init__(		self     :						Union[str, Any]				,	A     :						Union[str, Any]				,	A     :						Optional[int]				,	A     :						str				,	A     :						Union[str, Any]				,	A     :						int="train"				,	A     :						List[Any]=None				,	A     :						int=None				,	A     :						Tuple=None				,	A     :						str=""				,	)     ->					List[Any]:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.source')
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.target')
						_UpperCAmelCase	 =     self.get_char_lens(self.src_file)
						_UpperCAmelCase	 =     max_source_length
						_UpperCAmelCase	 =     max_target_length
						assert min(self.src_lens) > 0, F"found empty line in {self.src_file}"
						_UpperCAmelCase	 =     tokenizer
						_UpperCAmelCase	 =     prefix
						if n_obs is not None:
									_UpperCAmelCase	 =     self.src_lens[:n_obs]
						_UpperCAmelCase	 =     src_lang
						_UpperCAmelCase	 =     tgt_lang



			def __len__(		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						return len(self.src_lens)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     index + 1  # linecache starts at 1
						_UpperCAmelCase	 =     self.prefix + linecache.getline(str(self.src_file)				,	A).rstrip('\n')
						_UpperCAmelCase	 =     linecache.getline(str(self.tgt_file)				,	A).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				,	A):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						_UpperCAmelCase	 =     (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer				,	A) else self.tokenizer
						)
						_UpperCAmelCase	 =     self.tokenizer.generator if isinstance(self.tokenizer				,	A) else self.tokenizer

						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_source_length				,	'right')
						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_target_length				,	'right')

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



			@staticmethod
			def 					_lowerCamelCase (		A     :						str)     ->					Tuple:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						int				,	A     :						int)     ->					Dict[str, torch.Tensor]:



						"""simple docstring"""






						_UpperCAmelCase	 =     torch.stack([x['input_ids'] for x in batch])
						_UpperCAmelCase	 =     torch.stack([x['attention_mask'] for x in batch])
						_UpperCAmelCase	 =     torch.stack([x['decoder_input_ids'] for x in batch])
						_UpperCAmelCase	 =     (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer				,	A)
						    else self.tokenizer.pad_token_id
						)
						_UpperCAmelCase	 =     (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer				,	A)
						    else self.tokenizer.pad_token_id
						)
						_UpperCAmelCase	 =     trim_batch(A				,	A)
						_UpperCAmelCase ,  _UpperCAmelCase	 =     trim_batch(A				,	A				,	attention_mask=A)
						_UpperCAmelCase	 =     {
						    'input_ids': source_ids,
						    'attention_mask': source_mask,
						    'decoder_input_ids': y,
						}
						return batch





UpperCAmelCase__     = getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[List]					)       ->				Union[str, Any]:







			'''simple docstring'''
			return list(itertools.chain.from_iterable(_UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     str					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_git_info()
			save_json(_UpperCAmelCase							,     os.path.join(_UpperCAmelCase							,     'git_log.json'					)					)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[int]=4							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Dict:







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





def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase					) as f:
						return json.load(_UpperCAmelCase					)





def 							A			(  )       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     git.Repo(search_parent_directories=_UpperCAmelCase					)
			_UpperCAmelCase	 =     {
			    'repo_id': str(_UpperCAmelCase					),
			    'repo_sha': str(repo.head.object.hexsha					),
			    'repo_branch': str(repo.active_branch					),
			    'hostname': str(socket.gethostname()					),
			}
			return repo_infos





def 							A			(  _UpperCAmelCase       :     Callable							,     _UpperCAmelCase       :     Iterable					)       ->				List:







			'''simple docstring'''
			return list(map(_UpperCAmelCase							,     _UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Union[str, Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'wb'					) as f:
						return pickle.dump(_UpperCAmelCase							,     _UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''

			def remove_articles(_UpperCAmelCase       :     Optional[int]					):
						return re.sub(R'\b(a|an|the)\b'							,     ' '							,     _UpperCAmelCase					)

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

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

			def lower(_UpperCAmelCase       :     str					):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase					)					)					)					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     Counter(_UpperCAmelCase					) & Counter(_UpperCAmelCase					)
			_UpperCAmelCase	 =     sum(common.values()					)
			if num_same == 0:
						return 0
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     (2 * precision * recall) / (precision + recall)
			return fa





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			return normalize_answer(_UpperCAmelCase					) == normalize_answer(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     List[str]					)       ->				Dict:







			'''simple docstring'''
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     0
			for hypo, pred in zip(_UpperCAmelCase							,     _UpperCAmelCase					):
						em += exact_match_score(_UpperCAmelCase							,     _UpperCAmelCase					)
			if len(_UpperCAmelCase					) > 0:
						em /= len(_UpperCAmelCase					)
			return {"em": em}





def 							A			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				int:







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





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







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