




import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowerCamelCase   :						Optional[int]				=			get_tests_dir('''fixtures''')


class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Any:
								# A mock response for an HTTP head request to emulate server down
								__magic_name__							:	Dict    							=							mock.Mock()
								__magic_name__							:	int    							=							500
								__magic_name__							:	List[str]    							=							{}
								__magic_name__							:	str    							=							HTTPError
								__magic_name__							:	int    							=							{}

								# Download this model to make sure it's in the cache.
								__magic_name__							:	Dict    							=							WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''  )
								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch('''requests.Session.request'''       ,						return_value=lowerCamelCase_  ) as mock_head:
													__magic_name__							:	Any    							=							WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2'''  )
													# This check we did call the fake head request
													mock_head.assert_called()


			def 							UpperCAmelCase__       ( self			:   Dict  )						->   int:
								# This test is for deprecated behavior and can be removed in v5
								__magic_name__							:	Optional[int]    							=							WavaVecaFeatureExtractor.from_pretrained(
								    '''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'''  )






@is_staging_test
class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Any  )						->   Tuple:
								__magic_name__							:	Any    							=							TOKEN
								HfFolder.save_token(lowerCamelCase_  )

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Optional[Any]  )						->   List[str]:
								try:
													delete_repo(token=cls._token       ,						repo_id='''test-feature-extractor'''  )
								except HTTPError:
													pass

								try:
													delete_repo(token=cls._token       ,						repo_id='''valid_org/test-feature-extractor-org'''  )
								except HTTPError:
													pass

								try:
													delete_repo(token=cls._token       ,						repo_id='''test-dynamic-feature-extractor'''  )
								except HTTPError:
													pass

			def 							UpperCAmelCase__       ( self			:   Any  )						->   int:
								__magic_name__							:	List[Any]    							=							WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_  )
								feature_extractor.push_to_hub('''test-feature-extractor'''       ,						use_auth_token=self._token  )

								__magic_name__							:	List[str]    							=							WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''  )
								for k, v in feature_extractor.__dict__.items():
													self.assertEqual(lowerCamelCase_       ,						getattr(lowerCamelCase_       ,						lowerCamelCase_  )  )

								# Reset repo
								delete_repo(token=self._token       ,						repo_id='''test-feature-extractor'''  )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(
													    lowerCamelCase_       ,						repo_id='''test-feature-extractor'''       ,						push_to_hub=lowerCamelCase_       ,						use_auth_token=self._token  )

								__magic_name__							:	List[Any]    							=							WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''  )
								for k, v in feature_extractor.__dict__.items():
													self.assertEqual(lowerCamelCase_       ,						getattr(lowerCamelCase_       ,						lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Tuple:
								__magic_name__							:	Optional[Any]    							=							WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_  )
								feature_extractor.push_to_hub('''valid_org/test-feature-extractor'''       ,						use_auth_token=self._token  )

								__magic_name__							:	str    							=							WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor'''  )
								for k, v in feature_extractor.__dict__.items():
													self.assertEqual(lowerCamelCase_       ,						getattr(lowerCamelCase_       ,						lowerCamelCase_  )  )

								# Reset repo
								delete_repo(token=self._token       ,						repo_id='''valid_org/test-feature-extractor'''  )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(
													    lowerCamelCase_       ,						repo_id='''valid_org/test-feature-extractor-org'''       ,						push_to_hub=lowerCamelCase_       ,						use_auth_token=self._token  )

								__magic_name__							:	List[str]    							=							WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org'''  )
								for k, v in feature_extractor.__dict__.items():
													self.assertEqual(lowerCamelCase_       ,						getattr(lowerCamelCase_       ,						lowerCamelCase_  )  )


			def 							UpperCAmelCase__       ( self			:   Dict  )						->   int:
								CustomFeatureExtractor.register_for_auto_class()
								__magic_name__							:	Tuple    							=							CustomFeatureExtractor.from_pretrained(lowerCamelCase_  )

								feature_extractor.push_to_hub('''test-dynamic-feature-extractor'''       ,						use_auth_token=self._token  )

								# This has added the proper auto_map field to the config
								self.assertDictEqual(
								    feature_extractor.auto_map       ,						{'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''}       ,						)

								__magic_name__							:	List[Any]    							=							AutoFeatureExtractor.from_pretrained(
								    F'''{USER}/test-dynamic-feature-extractor'''       ,						trust_remote_code=lowerCamelCase_  )
								# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
								self.assertEqual(new_feature_extractor.__class__.__name__       ,						'''CustomFeatureExtractor'''  )









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



