



import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_lowerCAmelCase	:   Any    =   get_tests_dir("fixtures")
_lowerCAmelCase	:   List[Any]    =   get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowerCAmelCase	:   Union[str, Any]    =   get_tests_dir("fixtures/dummy-config.json")


class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  0
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'						)
						self.assertIsInstance(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_						)
						self.assertIsInstance(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						with tempfile.TemporaryDirectory() as tmpdirname:
										lowerCAmelCase__		  =  WavaVecaConfig()

										# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_						).to_dict()

										config_dict.pop('feature_extractor_type'						)
										lowerCAmelCase__		  =  WavaVecaFeatureExtractor(**a_						)

										# save in new folder
										model_config.save_pretrained(a_						)
										config.save_pretrained(a_						)

										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_						)

										# make sure private variable is not incorrectly saved
										lowerCAmelCase__		  =  json.loads(config.to_json_string()						)
										self.assertTrue('_processor_class' not in dict_as_saved						)

						self.assertIsInstance(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_						)
						self.assertIsInstance(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						with self.assertRaisesRegex(
						    a_    ,'bert-base is not a local folder and is not a valid model identifier'						):
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained('bert-base'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						with self.assertRaisesRegex(
						    a_    ,r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_    ,revision='aaaaaa'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						with self.assertRaisesRegex(
						    a_    ,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'    ,):
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(a_						):
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor'						)
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(a_						):
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=a_						)

						lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
						    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=a_						)
						self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'						)

						# Test feature extractor can be reloaded.
						with tempfile.TemporaryDirectory() as tmp_dir:
										feature_extractor.save_pretrained(a_						)
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_    ,trust_remote_code=a_						)
						self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,'NewFeatureExtractor'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						try:
										AutoConfig.register('custom'    ,a_						)
										AutoFeatureExtractor.register(a_    ,a_						)
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(a_						):
														AutoFeatureExtractor.register(a_    ,a_						)

										# Now that the config is registered, it can be used as any other config with the auto-API
										lowerCAmelCase__		  =  CustomFeatureExtractor.from_pretrained(a_						)
										with tempfile.TemporaryDirectory() as tmp_dir:
														feature_extractor.save_pretrained(a_						)
														lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(a_						)
														self.assertIsInstance(a_    ,a_						)

						finally:
										if "custom" in CONFIG_MAPPING._extra_content:
														del CONFIG_MAPPING._extra_content["custom"]
										if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
														del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""


						class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
								SCREAMING_SNAKE_CASE__								=	True

						try:
										AutoConfig.register('custom'    ,a_						)
										AutoFeatureExtractor.register(a_    ,a_						)
										# If remote code is not set, the default is to use local
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor'						)
										self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'						)
										self.assertTrue(feature_extractor.is_local						)

										# If remote code is disabled, we load the local one.
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=a_						)
										self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'						)
										self.assertTrue(feature_extractor.is_local						)

										# If remote is enabled, we load from the Hub
										lowerCAmelCase__		  =  AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=a_						)
										self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'						)
										self.assertTrue(not hasattr(a_    ,'is_local'						)						)

						finally:
										if "custom" in CONFIG_MAPPING._extra_content:
														del CONFIG_MAPPING._extra_content["custom"]
										if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
														del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								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
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





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