
'''simple docstring'''


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_		(      _lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase=True	,				_lowercase="pt" )   ->							Any:
					'''simple docstring'''

					lowerCamelCase_    :					Optional[Any]   =						{'''add_prefix_space''': True} if isinstance(__UpperCamelCase	,				__UpperCamelCase ) and not line.startswith(''' ''' ) else {}
					lowerCamelCase_    :					Union[str, Any]   =						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 lowercase_		(      _lowercase	,				_lowercase	,				_lowercase=None	,				)   ->							Dict:
					'''simple docstring'''

					lowerCamelCase_    :					str   =						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 		__lowercase (     _lowerCAmelCase			):

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

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

			def __getitem__(self	,      A					):
								lowerCamelCase_    :					Dict   =						index + 1  # linecache starts at 1
								lowerCamelCase_    :					List[str]   =						self.prefix + linecache.getline(str(self.src_file					)	,      _lowerCamelCase					).rstrip('''\n'''					)
								lowerCamelCase_    :					int   =						linecache.getline(str(self.tgt_file					)	,      _lowerCamelCase					).rstrip('''\n'''					)
								assert source_line, F"""empty source line for index {index}"""
								assert tgt_line, F"""empty tgt line for index {index}"""

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer	,      _lowerCamelCase					):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								lowerCamelCase_    :					Union[str, Any]   =						(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer	,      _lowerCamelCase					) else self.tokenizer
								)
								lowerCamelCase_    :					List[Any]   =						self.tokenizer.generator if isinstance(self.tokenizer	,      _lowerCamelCase					) else self.tokenizer

								lowerCamelCase_    :					Optional[Any]   =						encode_line(_lowerCamelCase	,      _lowerCamelCase	,      self.max_source_length	,      '''right'''					)
								lowerCamelCase_    :					Tuple   =						encode_line(_lowerCamelCase	,      _lowerCamelCase	,      self.max_target_length	,      '''right'''					)

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

			@staticmethod
			def 				UpperCAmelCase__       (A					):
								return [len(_lowerCamelCase					) for x in Path(_lowerCamelCase					).open().readlines()]






			def 				UpperCAmelCase__       (self	,      A					):
								lowerCamelCase_    :					List[Any]   =						torch.stack([x['''input_ids'''] for x in batch]					)
								lowerCamelCase_    :					Any   =						torch.stack([x['''attention_mask'''] for x in batch]					)
								lowerCamelCase_    :					Dict   =						torch.stack([x['''decoder_input_ids'''] for x in batch]					)
								lowerCamelCase_    :					Optional[Any]   =						(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer	,      _lowerCamelCase					)
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase_    :					Any   =						(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer	,      _lowerCamelCase					)
								    else self.tokenizer.pad_token_id
								)
								lowerCamelCase_    :					str   =						trim_batch(_lowerCamelCase	,      _lowerCamelCase					)
								lowerCamelCase_,					lowerCamelCase_    :					Optional[int]   =						trim_batch(_lowerCamelCase	,      _lowerCamelCase	,      attention_mask=_lowerCamelCase					)
								lowerCamelCase_    :					List[Any]   =						{
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch
__lowercase							:      str		 =   getLogger(__name__)




def lowercase_		(      _lowercase )   ->							Optional[int]:
					'''simple docstring'''

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




def lowercase_		(      _lowercase )   ->							str:
					'''simple docstring'''

					lowerCamelCase_    :					Tuple   =						get_git_info()
					save_json(__UpperCamelCase	,				os.path.join(__UpperCamelCase	,				'''git_log.json''' ) )




def lowercase_		(      _lowercase	,				_lowercase	,				_lowercase=4	,				**_lowercase )   ->							Optional[int]:
					'''simple docstring'''

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




def lowercase_		(      _lowercase )   ->							Any:
					'''simple docstring'''

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




def lowercase_		(      )   ->							str:
					'''simple docstring'''

					lowerCamelCase_    :					Any   =						git.Repo(search_parent_directories=__UpperCamelCase )
					lowerCamelCase_    :					Tuple   =						{
					    '''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 lowercase_		(      _lowercase	,				_lowercase )   ->							Optional[int]:
					'''simple docstring'''

					return list(map(__UpperCamelCase	,				__UpperCamelCase ) )




def lowercase_		(      _lowercase	,				_lowercase )   ->							Optional[Any]:
					'''simple docstring'''

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




def lowercase_		(      _lowercase )   ->							Any:
					'''simple docstring'''

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

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

					def remove_punc(_lowercase ):
										lowerCamelCase_    :					str   =						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(__UpperCamelCase ) ) ) )




def lowercase_		(      _lowercase	,				_lowercase )   ->							List[str]:
					'''simple docstring'''

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




def lowercase_		(      _lowercase	,				_lowercase )   ->							Optional[int]:
					'''simple docstring'''

					return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )




def lowercase_		(      _lowercase	,				_lowercase )   ->							Dict:
					'''simple docstring'''

					assert len(__UpperCamelCase ) == len(__UpperCamelCase )
					lowerCamelCase_    :					Any   =						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 lowercase_		(      _lowercase )   ->							Optional[Any]:
					'''simple docstring'''

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






def lowercase_		(      _lowercase	,				_lowercase	,				_lowercase )   ->							Optional[Any]:
					'''simple docstring'''

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










"""simple docstring"""


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 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     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 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= 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
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= 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
     __A									= 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
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= 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
     __A									= 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
     __A									= 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'''],
         ),       )




