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

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast





class 			_lowerCAmelCase      (			unittest.TestCase ):




				def   __a		(      self  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			0




				@slow
				def   __a		(      self  )       ->   Optional[Any]:
							for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
										self.assertIsNotNone(_UpperCamelCase  )
										self.assertIsInstance(_UpperCamelCase       ,       (BertTokenizer, BertTokenizerFast)  )
										self.assertGreater(len(_UpperCamelCase  )       ,       0  )

							for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
										self.assertIsNotNone(_UpperCamelCase  )
										self.assertIsInstance(_UpperCamelCase       ,       (GPTaTokenizer, GPTaTokenizerFast)  )
										self.assertGreater(len(_UpperCamelCase  )       ,       0  )




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       (BertTokenizer, BertTokenizerFast)  )
							self.assertEqual(tokenizer.vocab_size       ,       12  )




				def   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       (RobertaTokenizer, RobertaTokenizerFast)  )
							self.assertEqual(tokenizer.vocab_size       ,       20  )




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			AutoConfig.from_pretrained(_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )
							# Check that tokenizer_type ≠ model_type
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       config=_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       (BertTokenizer, BertTokenizerFast)  )
							self.assertEqual(tokenizer.vocab_size       ,       12  )




				def   __a		(      self  )       ->   List[Any]:
							with tempfile.TemporaryDirectory() as tmp_dir:
										shutil.copy("./tests/fixtures/vocab.txt"       ,       os.path.join(_UpperCamelCase       ,       "vocab.txt"  )  )

										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       tokenizer_type="bert"       ,       use_fast=_UpperCamelCase  )
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )

							with tempfile.TemporaryDirectory() as tmp_dir:
										shutil.copy("./tests/fixtures/vocab.json"       ,       os.path.join(_UpperCamelCase       ,       "vocab.json"  )  )
										shutil.copy("./tests/fixtures/merges.txt"       ,       os.path.join(_UpperCamelCase       ,       "merges.txt"  )  )

										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       tokenizer_type="gpt2"       ,       use_fast=_UpperCamelCase  )
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )




				@require_tokenizers
				def   __a		(      self  )       ->   str:
							with tempfile.TemporaryDirectory() as tmp_dir:
										shutil.copy("./tests/fixtures/vocab.txt"       ,       os.path.join(_UpperCamelCase       ,       "vocab.txt"  )  )

										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       tokenizer_type="bert"  )
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )

							with tempfile.TemporaryDirectory() as tmp_dir:
										shutil.copy("./tests/fixtures/vocab.json"       ,       os.path.join(_UpperCamelCase       ,       "vocab.json"  )  )
										shutil.copy("./tests/fixtures/merges.txt"       ,       os.path.join(_UpperCamelCase       ,       "merges.txt"  )  )

										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       tokenizer_type="gpt2"  )
										self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[int]:
							with pytest.raises(_UpperCamelCase  ):
										AutoTokenizer.from_pretrained("./"       ,       tokenizer_type="xxx"  )




				@require_tokenizers
				def   __a		(      self  )       ->   List[str]:
							for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
										lowerCAmelCase_      			=			tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased"  )
										self.assertIsInstance(_UpperCamelCase       ,       (BertTokenizer, BertTokenizerFast)  )

										if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													self.assertEqual(tokenizer.basic_tokenizer.do_lower_case       ,       _UpperCamelCase  )
										else:
													self.assertEqual(tokenizer.do_lower_case       ,       _UpperCamelCase  )

										self.assertEqual(tokenizer.model_max_length       ,       512  )




				@require_tokenizers
				def   __a		(      self  )       ->   List[str]:
							for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
										with self.assertRaisesRegex(
										    _UpperCamelCase       ,       "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"       ,       ):
													lowerCAmelCase_      			=			tokenizer_class.from_pretrained("julien-c/herlolip-not-exists"  )




				def   __a		(      self  )       ->   Any:
							# tests: https://github.com/huggingface/transformers/pull/13251
							# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
							# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
							lowerCAmelCase_      			=			TOKENIZER_MAPPING.values()
							lowerCAmelCase_      			=			[]

							for slow_tok, fast_tok in tokenizers:
										if slow_tok is not None:
													tokenizer_names.append(slow_tok.__name__  )

										if fast_tok is not None:
													tokenizer_names.append(fast_tok.__name__  )

							for tokenizer_name in tokenizer_names:
										# must find the right class
										tokenizer_class_from_name(_UpperCamelCase  )




				@require_tokenizers
				def   __a		(      self  )       ->   List[Any]:
							self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"       ,       use_fast=_UpperCamelCase  )       ,       _UpperCamelCase  )
							self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"  )       ,       _UpperCamelCase  )




				@require_tokenizers
				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("distilbert-base-uncased"       ,       do_lower_case=_UpperCamelCase  )
							lowerCAmelCase_      			=			"Hello, world. How are you?"
							lowerCAmelCase_      			=			tokenizer.tokenize(_UpperCamelCase  )
							self.assertEqual("[UNK]"       ,       tokens[0]  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("microsoft/mpnet-base"       ,       do_lower_case=_UpperCamelCase  )
							lowerCAmelCase_      			=			tokenizer.tokenize(_UpperCamelCase  )
							self.assertEqual("[UNK]"       ,       tokens[0]  )




				@require_tokenizers
				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config"  )
							self.assertEqual(type(_UpperCamelCase  )       ,       _UpperCamelCase  )
							self.assertEqual(tokenizer.model_max_length       ,       512  )
							self.assertEqual(tokenizer.vocab_size       ,       30_000  )
							self.assertEqual(tokenizer.unk_token       ,       "[UNK]"  )
							self.assertEqual(tokenizer.padding_side       ,       "right"  )
							self.assertEqual(tokenizer.truncation_side       ,       "right"  )




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							self.assertIsInstance(_UpperCamelCase       ,       (BertTokenizer, BertTokenizerFast)  )
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(_UpperCamelCase  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )

							self.assertIsInstance(_UpperCamelCase       ,       tokenizer.__class__  )
							self.assertEqual(tokenizera.vocab_size       ,       12  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("ctrl"  )
							# There is no fast CTRL so this always gives us a slow tokenizer.
							self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   int:
							# Check we can load the tokenizer config of an online model.
							lowerCAmelCase_      			=			get_tokenizer_config("bert-base-cased"  )
							lowerCAmelCase_      			=			config.pop("_commit_hash"       ,       _UpperCamelCase  )
							# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
							self.assertEqual(_UpperCamelCase       ,       {"do_lower_case": False}  )

							# This model does not have a tokenizer_config so we get back an empty dict.
							lowerCAmelCase_      			=			get_tokenizer_config(_UpperCamelCase  )
							self.assertDictEqual(_UpperCamelCase       ,       {}  )

							# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(_UpperCamelCase  )
										lowerCAmelCase_      			=			get_tokenizer_config(_UpperCamelCase  )

							# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
							self.assertEqual(config["tokenizer_class"]       ,       "BertTokenizer"  )




				def   __a		(      self  )       ->   List[Any]:
							try:
										AutoConfig.register("custom"       ,       _UpperCamelCase  )

										AutoTokenizer.register(_UpperCamelCase       ,       slow_tokenizer_class=_UpperCamelCase  )
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(_UpperCamelCase  ):
													AutoTokenizer.register(_UpperCamelCase       ,       slow_tokenizer_class=_UpperCamelCase  )

										lowerCAmelCase_      			=			CustomTokenizer.from_pretrained(_UpperCamelCase  )
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(_UpperCamelCase  )

													lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
													self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )

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




				@require_tokenizers
				def   __a		(      self  )       ->   str:
							try:
										AutoConfig.register("custom"       ,       _UpperCamelCase  )

										# Can register in two steps
										AutoTokenizer.register(_UpperCamelCase       ,       slow_tokenizer_class=_UpperCamelCase  )
										self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,       (CustomTokenizer, None)  )
										AutoTokenizer.register(_UpperCamelCase       ,       fast_tokenizer_class=_UpperCamelCase  )
										self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,       (CustomTokenizer, CustomTokenizerFast)  )

										del TOKENIZER_MAPPING._extra_content[CustomConfig]
										# Can register in one step
										AutoTokenizer.register(
										    _UpperCamelCase       ,       slow_tokenizer_class=_UpperCamelCase       ,       fast_tokenizer_class=_UpperCamelCase  )
										self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,       (CustomTokenizer, CustomTokenizerFast)  )

										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(_UpperCamelCase  ):
													AutoTokenizer.register(_UpperCamelCase       ,       fast_tokenizer_class=_UpperCamelCase  )

										# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
										# and that model does not have a tokenizer.json
										with tempfile.TemporaryDirectory() as tmp_dir:
													lowerCAmelCase_      			=			BertTokenizerFast.from_pretrained(_UpperCamelCase  )
													bert_tokenizer.save_pretrained(_UpperCamelCase  )
													lowerCAmelCase_      			=			CustomTokenizerFast.from_pretrained(_UpperCamelCase  )

										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(_UpperCamelCase  )

													lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
													self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )

													lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
													self.assertIsInstance(_UpperCamelCase       ,       _UpperCamelCase  )

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




				def   __a		(      self  )       ->   Optional[Any]:
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(_UpperCamelCase  ):
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"  )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(_UpperCamelCase  ):
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase  )
							self.assertTrue(tokenizer.special_attribute_present  )
							# Test tokenizer can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(_UpperCamelCase  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       trust_remote_code=_UpperCamelCase  )
							self.assertTrue(reloaded_tokenizer.special_attribute_present  )

							if is_tokenizers_available():
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )
										self.assertEqual(reloaded_tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )

										# Test we can also load the slow version
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
										self.assertTrue(tokenizer.special_attribute_present  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										# Test tokenizer can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(_UpperCamelCase  )
													lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       trust_remote_code=_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
										self.assertEqual(reloaded_tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										self.assertTrue(reloaded_tokenizer.special_attribute_present  )
							else:
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										self.assertEqual(reloaded_tokenizer.__class__.__name__       ,       "NewTokenizer"  )




				@require_tokenizers
				def   __a		(      self  )       ->   List[Any]:





							class 			_lowerCAmelCase      (			__a ):
											_lowercase    	=False





							class 			_lowerCAmelCase      (			__a ):
											_lowercase    	=NewTokenizer
											_lowercase    	=False

							try:
										AutoConfig.register("custom"       ,       _UpperCamelCase  )
										AutoTokenizer.register(_UpperCamelCase       ,       slow_tokenizer_class=_UpperCamelCase  )
										AutoTokenizer.register(_UpperCamelCase       ,       fast_tokenizer_class=_UpperCamelCase  )
										# If remote code is not set, the default is to use local
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )
										self.assertFalse(tokenizer.special_attribute_present  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"       ,       use_fast=_UpperCamelCase  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										self.assertFalse(tokenizer.special_attribute_present  )

										# If remote code is disabled, we load the local one.
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )
										self.assertFalse(tokenizer.special_attribute_present  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										self.assertFalse(tokenizer.special_attribute_present  )

										# If remote is enabled, we load from the Hub
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )
										self.assertTrue(tokenizer.special_attribute_present  )
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer"       ,       trust_remote_code=_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
										self.assertTrue(tokenizer.special_attribute_present  )

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




				def   __a		(      self  )       ->   Any:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
							    "hf-internal-testing/test_dynamic_tokenizer_legacy"       ,       trust_remote_code=_UpperCamelCase  )
							self.assertTrue(tokenizer.special_attribute_present  )
							if is_tokenizers_available():
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizerFast"  )

										# Test we can also load the slow version
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(
										    "hf-internal-testing/test_dynamic_tokenizer_legacy"       ,       trust_remote_code=_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
										self.assertTrue(tokenizer.special_attribute_present  )
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )
							else:
										self.assertEqual(tokenizer.__class__.__name__       ,       "NewTokenizer"  )




				def   __a		(      self  )       ->   str:
							with self.assertRaisesRegex(
							    _UpperCamelCase       ,       "bert-base is not a local folder and is not a valid model identifier"  ):
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("bert-base"  )




				def   __a		(      self  )       ->   List[str]:
							with self.assertRaisesRegex(
							    _UpperCamelCase       ,       r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"  ):
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       revision="aaaaaa"  )




				def   __a		(      self  )       ->   int:
							# Make sure we have cached the tokenizer.
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"  )
							with RequestCounter() as counter:
										lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"  )
										self.assertEqual(counter.get_request_count       ,       0  )
										self.assertEqual(counter.head_request_count       ,       1  )
										self.assertEqual(counter.other_request_count       ,       0  )





from ...processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''SpeechT5FeatureExtractor'''
				_lowercase    	='''SpeechT5Tokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("audio_target"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )

							if audio is not None and text is not None:
										raise ValueError(
										    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
							if audio_target is not None and text_target is not None:
										raise ValueError(
										    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
							if audio is None and audio_target is None and text is None and text_target is None:
										raise ValueError(
										    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							elif text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if audio_target is not None:
										lowerCAmelCase_      			=			self.feature_extractor(audio_target=_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_values"]
							elif text_target is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			targets["input_ids"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			kwargs.pop("input_values"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("input_ids"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )

							if input_values is not None and input_ids is not None:
										raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
							if input_values is None and input_ids is None and labels is None:
										raise ValueError(
										    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

							if input_values is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							elif input_ids is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			None

							if labels is not None:
										if "input_ids" in labels or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and "input_ids" in labels[0]):
													lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			targets["input_ids"]
										else:
													lowerCAmelCase_      			=			self.feature_extractor.feature_size
													lowerCAmelCase_      			=			self.feature_extractor.num_mel_bins
													lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
													lowerCAmelCase_      			=			feature_size_hack
													lowerCAmelCase_      			=			targets["input_values"]
							else:
										lowerCAmelCase_      			=			None

							if inputs is None:
										return targets

							if targets is not None:
										lowerCAmelCase_      			=			labels

										lowerCAmelCase_      			=			targets.get("attention_mask"  )
										if decoder_attention_mask is not None:
													lowerCAmelCase_      			=			decoder_attention_mask

							return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )



