






import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


lowercase  							=					get_tests_dir("fixtures/dummy_feature_extractor_config.json")
lowercase  							=					get_tests_dir("fixtures/vocab.json")
lowercase  							=					get_tests_dir("fixtures")




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							0




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'					)
								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:




								with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_				=							WavaVecaConfig()
															snake_case_				=							AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'					)

															# save in new folder
															model_config.save_pretrained(__SCREAMING_SNAKE_CASE					)
															processor.save_pretrained(__SCREAMING_SNAKE_CASE					)

															snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:




								with tempfile.TemporaryDirectory() as tmpdirname:
															# copy relevant files
															copyfile(__SCREAMING_SNAKE_CASE       ,			os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)					)
															copyfile(__SCREAMING_SNAKE_CASE       ,			os.path.join(__SCREAMING_SNAKE_CASE       ,			'vocab.json'					)					)

															snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:




								with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_				=							WavaVecaFeatureExtractor()
															snake_case_				=							AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'					)

															snake_case_				=							WavaVecaProcessor(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

															# save in new folder
															processor.save_pretrained(__SCREAMING_SNAKE_CASE					)

															# drop `processor_class` in tokenizer
															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)       ,			'r'					) as f:
																						snake_case_				=							json.load(__SCREAMING_SNAKE_CASE					)
																						config_dict.pop('processor_class'					)

															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)       ,			'w'					) as f:
																						f.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)

															snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    str:




								with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_				=							WavaVecaFeatureExtractor()
															snake_case_				=							AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'					)

															snake_case_				=							WavaVecaProcessor(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

															# save in new folder
															processor.save_pretrained(__SCREAMING_SNAKE_CASE					)

															# drop `processor_class` in feature extractor
															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)       ,			'r'					) as f:
																						snake_case_				=							json.load(__SCREAMING_SNAKE_CASE					)
																						config_dict.pop('processor_class'					)

															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)       ,			'w'					) as f:
																						f.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)

															snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:




								with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_				=							WavaVecaConfig(processor_class='Wav2Vec2Processor'					)
															model_config.save_pretrained(__SCREAMING_SNAKE_CASE					)
															# copy relevant files
															copyfile(__SCREAMING_SNAKE_CASE       ,			os.path.join(__SCREAMING_SNAKE_CASE       ,			'vocab.json'					)					)
															# create emtpy sample processor
															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)       ,			'w'					) as f:
																						f.write('{}'					)

															snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:




								with self.assertRaises(__SCREAMING_SNAKE_CASE					):
															snake_case_				=							AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__SCREAMING_SNAKE_CASE					):
															snake_case_				=							AutoProcessor.from_pretrained(
															    'hf-internal-testing/test_dynamic_processor'       ,			trust_remote_code=__SCREAMING_SNAKE_CASE					)

								snake_case_				=							AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'       ,			trust_remote_code=__SCREAMING_SNAKE_CASE					)
								self.assertTrue(processor.special_attribute_present					)
								self.assertEqual(processor.__class__.__name__       ,			'NewProcessor'					)

								snake_case_				=							processor.feature_extractor
								self.assertTrue(feature_extractor.special_attribute_present					)
								self.assertEqual(feature_extractor.__class__.__name__       ,			'NewFeatureExtractor'					)

								snake_case_				=							processor.tokenizer
								self.assertTrue(tokenizer.special_attribute_present					)
								if is_tokenizers_available():
															self.assertEqual(tokenizer.__class__.__name__       ,			'NewTokenizerFast'					)

															# Test we can also load the slow version
															snake_case_				=							AutoProcessor.from_pretrained(
															    'hf-internal-testing/test_dynamic_processor'       ,			trust_remote_code=__SCREAMING_SNAKE_CASE       ,			use_fast=__SCREAMING_SNAKE_CASE					)
															snake_case_				=							new_processor.tokenizer
															self.assertTrue(new_tokenizer.special_attribute_present					)
															self.assertEqual(new_tokenizer.__class__.__name__       ,			'NewTokenizer'					)
								else:
															self.assertEqual(tokenizer.__class__.__name__       ,			'NewTokenizer'					)




	def 							_UpperCamelCase			(       self					)       ->    Any:




								try:
															AutoConfig.register('custom'       ,			__SCREAMING_SNAKE_CASE					)
															AutoFeatureExtractor.register(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)
															AutoTokenizer.register(__SCREAMING_SNAKE_CASE       ,			slow_tokenizer_class=__SCREAMING_SNAKE_CASE					)
															AutoProcessor.register(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)
															# Trying to register something existing in the Transformers library will raise an error
															with self.assertRaises(__SCREAMING_SNAKE_CASE					):
																						AutoProcessor.register(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

															# Now that the config is registered, it can be used as any other config with the auto-API
															snake_case_				=							CustomFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE					)

															with tempfile.TemporaryDirectory() as tmp_dir:
																						snake_case_				=							os.path.join(__SCREAMING_SNAKE_CASE       ,			'vocab.txt'					)
																						with open(__SCREAMING_SNAKE_CASE       ,			'w'       ,			encoding='utf-8'					) as vocab_writer:
																													vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]					)					)
																						snake_case_				=							CustomTokenizer(__SCREAMING_SNAKE_CASE					)

															snake_case_				=							CustomProcessor(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

															with tempfile.TemporaryDirectory() as tmp_dir:
																						processor.save_pretrained(__SCREAMING_SNAKE_CASE					)
																						snake_case_				=							AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)
																						self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

								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]
															if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]
															if CustomConfig in PROCESSOR_MAPPING._extra_content:
																						del PROCESSOR_MAPPING._extra_content[CustomConfig]




	def 							_UpperCamelCase			(       self					)       ->    Tuple:




								class  UpperCamelCase_      (			snake_case_				):



									'''simple docstring'''




									lowerCAmelCase													= False






								class  UpperCamelCase_      (			snake_case_				):



									'''simple docstring'''




									lowerCAmelCase													= False






								class  UpperCamelCase_      (			snake_case_				):



									'''simple docstring'''




									lowerCAmelCase													= '''AutoFeatureExtractor'''
									lowerCAmelCase													= '''AutoTokenizer'''
									lowerCAmelCase													= False

								try:
															AutoConfig.register('custom'       ,			__SCREAMING_SNAKE_CASE					)
															AutoFeatureExtractor.register(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)
															AutoTokenizer.register(__SCREAMING_SNAKE_CASE       ,			slow_tokenizer_class=__SCREAMING_SNAKE_CASE					)
															AutoProcessor.register(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)
															# If remote code is not set, the default is to use local classes.
															snake_case_				=							AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					)
															self.assertEqual(processor.__class__.__name__       ,			'NewProcessor'					)
															self.assertFalse(processor.special_attribute_present					)
															self.assertFalse(processor.feature_extractor.special_attribute_present					)
															self.assertFalse(processor.tokenizer.special_attribute_present					)

															# If remote code is disabled, we load the local ones.
															snake_case_				=							AutoProcessor.from_pretrained(
															    'hf-internal-testing/test_dynamic_processor'       ,			trust_remote_code=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(processor.__class__.__name__       ,			'NewProcessor'					)
															self.assertFalse(processor.special_attribute_present					)
															self.assertFalse(processor.feature_extractor.special_attribute_present					)
															self.assertFalse(processor.tokenizer.special_attribute_present					)

															# If remote is enabled, we load from the Hub.
															snake_case_				=							AutoProcessor.from_pretrained(
															    'hf-internal-testing/test_dynamic_processor'       ,			trust_remote_code=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(processor.__class__.__name__       ,			'NewProcessor'					)
															self.assertTrue(processor.special_attribute_present					)
															self.assertTrue(processor.feature_extractor.special_attribute_present					)
															self.assertTrue(processor.tokenizer.special_attribute_present					)

								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]
															if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]
															if CustomConfig in PROCESSOR_MAPPING._extra_content:
																						del PROCESSOR_MAPPING._extra_content[CustomConfig]




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'					)
								self.assertEqual(processor.__class__.__name__       ,			'BertTokenizerFast'					)






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'					)
								self.assertEqual(processor.__class__.__name__       ,			'ConvNextImageProcessor'					)






@is_staging_test
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']




	@classmethod
	def 							_UpperCamelCase			(       cls					)       ->    Dict:
								snake_case_				=							TOKEN
								HfFolder.save_token(__SCREAMING_SNAKE_CASE					)




	@classmethod
	def 							_UpperCamelCase			(       cls					)       ->    int:




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

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

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




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_				=							WavaVecaProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)
								with tempfile.TemporaryDirectory() as tmp_dir:
															processor.save_pretrained(
															    os.path.join(__SCREAMING_SNAKE_CASE       ,			'test-processor'					)       ,			push_to_hub=__SCREAMING_SNAKE_CASE       ,			use_auth_token=self._token					)

															snake_case_				=							WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor'''					)
															for k, v in processor.feature_extractor.__dict__.items():
																						self.assertEqual(__SCREAMING_SNAKE_CASE       ,			getattr(new_processor.feature_extractor       ,			__SCREAMING_SNAKE_CASE					)					)
															self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,			processor.tokenizer.get_vocab()					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							WavaVecaProcessor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								with tempfile.TemporaryDirectory() as tmp_dir:
															processor.save_pretrained(
															    os.path.join(__SCREAMING_SNAKE_CASE       ,			'test-processor-org'					)       ,			push_to_hub=__SCREAMING_SNAKE_CASE       ,			use_auth_token=self._token       ,			organization='valid_org'       ,			)

															snake_case_				=							WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'					)
															for k, v in processor.feature_extractor.__dict__.items():
																						self.assertEqual(__SCREAMING_SNAKE_CASE       ,			getattr(new_processor.feature_extractor       ,			__SCREAMING_SNAKE_CASE					)					)
															self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,			processor.tokenizer.get_vocab()					)






	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								CustomFeatureExtractor.register_for_auto_class()
								CustomTokenizer.register_for_auto_class()
								CustomProcessor.register_for_auto_class()

								snake_case_				=							CustomFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE					)

								with tempfile.TemporaryDirectory() as tmp_dir:
															snake_case_				=							os.path.join(__SCREAMING_SNAKE_CASE       ,			'vocab.txt'					)
															with open(__SCREAMING_SNAKE_CASE       ,			'w'       ,			encoding='utf-8'					) as vocab_writer:
																						vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]					)					)
															snake_case_				=							CustomTokenizer(__SCREAMING_SNAKE_CASE					)

								snake_case_				=							CustomProcessor(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE					)

								with tempfile.TemporaryDirectory() as tmp_dir:
															create_repo(F'''{USER}/test-dynamic-processor'''       ,			token=self._token					)
															snake_case_				=							Repository(__SCREAMING_SNAKE_CASE       ,			clone_from=F'''{USER}/test-dynamic-processor'''       ,			token=self._token					)
															processor.save_pretrained(__SCREAMING_SNAKE_CASE					)

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

															# This has added the proper auto_map field to the tokenizer config
															with open(os.path.join(__SCREAMING_SNAKE_CASE       ,			'tokenizer_config.json'					)					) as f:
																						snake_case_				=							json.load(__SCREAMING_SNAKE_CASE					)
															self.assertDictEqual(
															    tokenizer_config['auto_map']       ,			{
															        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
															        'AutoProcessor': 'custom_processing.CustomProcessor',
															    }       ,			)

															# The code has been copied from fixtures
															self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE       ,			'custom_feature_extraction.py'					)					)					)
															self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE       ,			'custom_tokenization.py'					)					)					)
															self.assertTrue(os.path.isfile(os.path.join(__SCREAMING_SNAKE_CASE       ,			'custom_processing.py'					)					)					)

															repo.push_to_hub()

								snake_case_				=							AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor'''       ,			trust_remote_code=__SCREAMING_SNAKE_CASE					)
								# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
								self.assertEqual(new_processor.__class__.__name__       ,			'CustomProcessor'					)








'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

UpperCAmelCase :       int			   =     random.Random()







def    a__       (  a__      ,    a__=1.0      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     if rng is None:
          __SCREAMING_SNAKE_CASE     =      global_rng

     __SCREAMING_SNAKE_CASE     =      []
     for batch_idx in range(shape[0]						):
          values.append([]						)
          for _ in range(shape[1]						):
               values[-1].append(rng.random() * scale						)

     return values







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str=7					,							__SCREAMING_SNAKE_CASE					:       List[str]=400					,							__SCREAMING_SNAKE_CASE					:       Any=2_000					,							__SCREAMING_SNAKE_CASE					:       List[str]=10					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=160					,							__SCREAMING_SNAKE_CASE					:       List[str]=8					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       Dict=4_000					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      min_seq_length
      __SCREAMING_SNAKE_CASE     =      max_seq_length
      __SCREAMING_SNAKE_CASE     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __SCREAMING_SNAKE_CASE     =      padding_value
      __SCREAMING_SNAKE_CASE     =      sampling_rate
      __SCREAMING_SNAKE_CASE     =      return_attention_mask
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      feature_size
      __SCREAMING_SNAKE_CASE     =      chunk_length
      __SCREAMING_SNAKE_CASE     =      hop_length
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Dict:


      """simple docstring"""




      return {
          "feature_size": self.feature_size,
          "hop_length": self.hop_length,
          "chunk_length": self.chunk_length,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }


 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=False					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=False			)  -> Union[str, Any]:


      """simple docstring"""




      def _flatten(__SCREAMING_SNAKE_CASE					:       Dict			):
           return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)

      if equal_length:
           __SCREAMING_SNAKE_CASE     =      [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
      else:
           # make sure that inputs increase in size
           __SCREAMING_SNAKE_CASE     =      [
               floats_list((x, self.feature_size)			)
               for x in range(self.min_seq_length					,							self.max_seq_length					,							self.seq_length_diff			)
           ]
      if numpify:
           __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
      return speech_inputs







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      WhisperFeatureExtractor if is_speech_available() else None
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractionTester(self			)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE			)[0]
           check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      os.path.join(__SCREAMING_SNAKE_CASE					,							"""feat_extract.json"""			)
           feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      # Test feature size
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							padding="""max_length"""					,							return_tensors="""np"""			).input_features
      self.assertTrue(input_features.ndim == 3			)
      self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
      self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

      # Test not batched input
      __SCREAMING_SNAKE_CASE     =      feature_extractor(speech_inputs[0]					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(np_speech_inputs[0]					,							return_tensors="""np"""			).input_features
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test batched
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test 2-D numpy arrays are batched.
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
      __SCREAMING_SNAKE_CASE     =      np.asarray(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test truncation required
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(200					,							(feature_extractor.n_samples + 500)					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      __SCREAMING_SNAKE_CASE     =      [x[: feature_extractor.n_samples] for x in speech_inputs]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs_truncated]

      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      import torch

      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      np.random.rand(100					,							32			).astype(np.floataa			)
      __SCREAMING_SNAKE_CASE     =      np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
           __SCREAMING_SNAKE_CASE     =      feature_extractor.pad([{"""input_features""": inputs}]					,							return_tensors="""np"""			)
           self.assertTrue(np_processed.input_features.dtype == np.floataa			)
           __SCREAMING_SNAKE_CASE     =      feature_extractor.pad([{"""input_features""": inputs}]					,							return_tensors="""pt"""			)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_dataset("""hf-internal-testing/librispeech_asr_dummy"""					,							"""clean"""					,							split="""validation"""			)
      # automatic decoding with librispeech
      __SCREAMING_SNAKE_CASE     =      ds.sort("""id"""			).select(range(__SCREAMING_SNAKE_CASE			)			)[:num_samples]["""audio"""]

      return [x["array"] for x in speech_samples]
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      torch.tensor(
          [
              0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
              0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
              0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
              -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
          ]			)
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)
      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractor()
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).input_features
      self.assertEqual(input_features.shape					,							(1, 80, 3_000)			)
      self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)[0]
      __SCREAMING_SNAKE_CASE     =      ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
      __SCREAMING_SNAKE_CASE     =      feat_extract.zero_mean_unit_var_norm([audio]					,							attention_mask=__SCREAMING_SNAKE_CASE			)[0]

      self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE			) < 1E-3			)			)
      self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE			) - 1			) < 1E-3			)			)






