


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


UpperCAmelCase_				:			Any   =	get_tests_dir('fixtures')
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Tuple:
             # A mock response for an HTTP head request to emulate server down
             a_ :		Tuple							=  mock.Mock()
             a_ :		Optional[int]							=  5_0_0
             a_ :		Union[str, Any]							=  {}
             a_ :		int							=  HTTPError
             a_ :		List[Any]							=  {}

             # Download this model to make sure it's in the cache.
             a_ :		Tuple							=  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=SCREAMING_SNAKE_CASE__					) as mock_head:
                    a_ :		str							=  WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
                    # This check we did call the fake head request
                    mock_head.assert_called()

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
             # This test is for deprecated behavior and can be removed in v5
             a_ :		Tuple							=  WavaVecaFeatureExtractor.from_pretrained(
                 'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'					)
@is_staging_test
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Optional[Any]					)		->      Any:
             a_ :		int							=  TOKEN
             HfFolder.save_token(SCREAMING_SNAKE_CASE__					)
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	int					)		->      Optional[int]:
             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 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[Any]:
             a_ :		Any							=  WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					)
             feature_extractor.push_to_hub('test-feature-extractor'					,							use_auth_token=self._token					)

             a_ :		Tuple							=  WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""					)
             for k, v in feature_extractor.__dict__.items():
                    self.assertEqual(SCREAMING_SNAKE_CASE__					,							getattr(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)

             # 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(
                        SCREAMING_SNAKE_CASE__					,							repo_id='test-feature-extractor'					,							push_to_hub=SCREAMING_SNAKE_CASE__					,							use_auth_token=self._token					)

             a_ :		Tuple							=  WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor"""					)
             for k, v in feature_extractor.__dict__.items():
                    self.assertEqual(SCREAMING_SNAKE_CASE__					,							getattr(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		Union[str, Any]							=  WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					)
             feature_extractor.push_to_hub('valid_org/test-feature-extractor'					,							use_auth_token=self._token					)

             a_ :		Union[str, Any]							=  WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'					)
             for k, v in feature_extractor.__dict__.items():
                    self.assertEqual(SCREAMING_SNAKE_CASE__					,							getattr(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)					)

             # 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(
                        SCREAMING_SNAKE_CASE__					,							repo_id='valid_org/test-feature-extractor-org'					,							push_to_hub=SCREAMING_SNAKE_CASE__					,							use_auth_token=self._token					)

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
             CustomFeatureExtractor.register_for_auto_class()
             a_ :		List[Any]							=  CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					)

             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'}					,							)

             a_ :		Tuple							=  AutoFeatureExtractor.from_pretrained(
                 F"""{USER}/test-dynamic-feature-extractor"""					,							trust_remote_code=SCREAMING_SNAKE_CASE__					)
             # 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 os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


