

"""simple docstring"""





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 DetaImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						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			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Optional[Any]  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						List[str]  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Any  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	image_processing(lowercase_   ,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			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						List[str]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						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			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)










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_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Optional[Any]=True			,			__UpperCAmelCase						:	Any="pt"							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			{'add_prefix_space': True} if isinstance(__UpperCAmelCase			,			__UpperCAmelCase							) and not line.startswith(' '							) else {}
      SCREAMING_SNAKE_CASE_							=			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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Dict=None			,			)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			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_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







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



 def __len__(							self  :		Tuple						):
       return len(self.src_lens						)



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

       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_source_length				,				'right'						)
       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_target_length				,				'right'						)

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



 @staticmethod
 def       lowerCAmelCase_  (							_lowerCAmelCase  :		Optional[int]						):
       return [len(_lowerCAmelCase						) for x in Path(_lowerCAmelCase						).open().readlines()]




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


lowerCamelCase__ :   List[str]					=							getLogger(__name__)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[List]							)					-> Tuple:
      return list(itertools.chain.from_iterable(__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> None:
      SCREAMING_SNAKE_CASE_							=			get_git_info()
      save_json(__UpperCAmelCase			,			os.path.join(__UpperCAmelCase			,			'git_log.json'							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Tuple			,			__UpperCAmelCase						:	Optional[int]=4			,			**__UpperCAmelCase						:	Tuple							)					-> str:
      with open(__UpperCAmelCase			,			'w'							) as f:
            json.dump(__UpperCAmelCase			,			__UpperCAmelCase			,			indent=__UpperCAmelCase			,			**__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]							)					-> int:
      with open(__UpperCAmelCase							) as f:
            return json.load(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      )					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			git.Repo(search_parent_directories=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			{
          '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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Callable			,			__UpperCAmelCase						:	Iterable							)					-> List:
      return list(map(__UpperCAmelCase			,			__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Dict							)					-> Dict:
      with open(__UpperCAmelCase			,			'wb'							) as f:
            return pickle.dump(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Any:

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

      def white_space_fix(__UpperCAmelCase						:	List[str]							):
            return " ".join(text.split()							)

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

      def lower(__UpperCAmelCase						:	Union[str, Any]							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase							)							)							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Any							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			Counter(__UpperCAmelCase							) & Counter(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			sum(common.values()							)
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			(2 * precision * recall) / (precision + recall)
      return fa

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Optional[int]							)					-> Any:
      return normalize_answer(__UpperCAmelCase							) == normalize_answer(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	List[str]							)					-> Dict:
      assert len(__UpperCAmelCase							) == len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]							)					-> Dict:
      return model_prefix.startswith('rag'							)





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	List[str]							)					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      SCREAMING_SNAKE_CASE_							=			'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
                  SCREAMING_SNAKE_CASE_							=			p if hasattr(__UpperCAmelCase			,			__UpperCAmelCase							) else equivalent_param[p]
                  setattr(__UpperCAmelCase			,			__UpperCAmelCase			,			getattr(__UpperCAmelCase			,			__UpperCAmelCase							)							)
                  delattr(__UpperCAmelCase			,			__UpperCAmelCase							)
      return hparams, config