



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase__			= logging.get_logger(__name__)


lowerCamelCase__			= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__			= {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__			= {
    """allenai/led-base-16384""": 1_6384,
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[str]    =VOCAB_FILES_NAMES
							__lowerCamelCase	:	Tuple    =PRETRAINED_VOCAB_FILES_MAP
							__lowerCamelCase	:	int    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCamelCase	:	str    =LEDTokenizer
							__lowerCamelCase	:	Dict    =['input_ids', 'attention_mask']





							def __init__(   self		:		Tuple      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		Tuple=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		Optional[int]="replace"      ,				__lowercase		:		str="<s>"      ,				__lowercase		:		Dict="</s>"      ,				__lowercase		:		Any="</s>"      ,				__lowercase		:		Any="<s>"      ,				__lowercase		:		Any="<unk>"      ,				__lowercase		:		Tuple="<pad>"      ,				__lowercase		:		Union[str, Any]="<mask>"      ,				__lowercase		:		Tuple=False      ,				__lowercase		:		List[Any]=True      ,				**__lowercase		:		Optional[int]      ,				):

											'''simple docstring'''
											super().__init__(
											    __lowercase      ,				__lowercase      ,				tokenizer_file=__lowercase      ,				errors=__lowercase      ,				bos_token=__lowercase      ,				eos_token=__lowercase      ,				sep_token=__lowercase      ,				cls_token=__lowercase      ,				unk_token=__lowercase      ,				pad_token=__lowercase      ,				mask_token=__lowercase      ,				add_prefix_space=__lowercase      ,				trim_offsets=__lowercase      ,				**__lowercase      ,				)

											__a       =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
											if pre_tok_state.get("""add_prefix_space"""      ,				__lowercase	) != add_prefix_space:
															__a       =		getattr(__lowercase      ,				pre_tok_state.pop("""type"""	)	)
															__a       =		add_prefix_space
															__a       =		pre_tok_class(**__lowercase	)

											__a       =		add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											__a       =		"""post_processor"""
											__a       =		getattr(self.backend_tokenizer      ,				__lowercase      ,				__lowercase	)
											if tokenizer_component_instance:
															__a       =		json.loads(tokenizer_component_instance.__getstate__()	)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																			__a       =		tuple(state["""sep"""]	)
															if "cls" in state:
																			__a       =		tuple(state["""cls"""]	)

															__a       =		False

															if state.get("""add_prefix_space"""      ,				__lowercase	) != add_prefix_space:
																			__a       =		add_prefix_space
																			__a       =		True

															if state.get("""trim_offsets"""      ,				__lowercase	) != trim_offsets:
																			__a       =		trim_offsets
																			__a       =		True

															if changes_to_apply:
																			__a       =		getattr(__lowercase      ,				state.pop("""type"""	)	)
																			__a       =		component_class(**__lowercase	)
																			setattr(self.backend_tokenizer      ,				__lowercase      ,				__lowercase	)





							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											if self._mask_token is None:
															if self.verbose:
																			logger.error("""Using mask_token, but it is not set yet."""	)
															return None
											return str(self._mask_token	)





							@mask_token.setter
							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		AddedToken(__lowercase      ,				lstrip=__lowercase      ,				rstrip=__lowercase	) if isinstance(__lowercase      ,				__lowercase	) else value
											__a       =		value





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				*__lowercase		:		Union[str, Any]      ,				**__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		kwargs.get("""is_split_into_words"""      ,				__lowercase	)

											if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""	)

											return super()._batch_encode_plus(*__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[str]      ,				*__lowercase		:		Optional[int]      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		kwargs.get("""is_split_into_words"""      ,				__lowercase	)

											if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
															    """to use it with pretokenized inputs."""	)

											return super()._encode_plus(*__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		Optional[str] = None	):

											'''simple docstring'''
											__a       =		self._tokenizer.model.save(__lowercase      ,				name=__lowercase	)
											return tuple(__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		List[Any]=None	):

											'''simple docstring'''
											__a       =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
															return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None	):

											'''simple docstring'''
											__a       =		[self.sep_token_id]
											__a       =		[self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		Union[Dict[str, EncodedInput], BatchEncoding]      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[bool] = None      ,				):

											'''simple docstring'''
											__a       =		super()._pad(
											    encoded_inputs=__lowercase      ,				max_length=__lowercase      ,				padding_strategy=__lowercase      ,				pad_to_multiple_of=__lowercase      ,				return_attention_mask=__lowercase      ,				)

											# Load from model defaults
											if return_attention_mask is None:
															__a       =		"""attention_mask""" in self.model_input_names

											if return_attention_mask and "global_attention_mask" in encoded_inputs:
															__a       =		encoded_inputs[self.model_input_names[0]]
															# `global_attention_mask` need to have the same length as other (sequential) inputs.
															__a       =		len(encoded_inputs["""global_attention_mask"""]	) != len(__lowercase	)

															if needs_to_be_padded:
																			__a       =		len(__lowercase	) - len(encoded_inputs["""global_attention_mask"""]	)

																			if self.padding_side == "right":
																							# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																							__a       =		(
																							    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																							)
																			elif self.padding_side == "left":
																							__a       =		[-1] * difference + encoded_inputs[
																							    """global_attention_mask"""
																							]
																			else:
																							raise ValueError("""Invalid padding strategy:""" + str(self.padding_side	)	)

											return encoded_inputs





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

lowerCamelCase__			= random.Random()

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int=1.0				,     _SCREAMING_SNAKE_CASE       :			Any=None				,     _SCREAMING_SNAKE_CASE       :			Any=None    ):
				"""simple docstring"""







				if rng is None:
								__a       =		global_rng

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

				return values






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Dict      ,				__lowercase		:		Tuple      ,				__lowercase		:		Tuple=7      ,				__lowercase		:		Optional[int]=400      ,				__lowercase		:		int=2000      ,				__lowercase		:		List[Any]=2048      ,				__lowercase		:		List[str]=128      ,				__lowercase		:		Union[str, Any]=1      ,				__lowercase		:		str=512      ,				__lowercase		:		List[str]=30      ,				__lowercase		:		Tuple=44100      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		min_seq_length
											__a       =		max_seq_length
											__a       =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__a       =		spectrogram_length
											__a       =		feature_size
											__a       =		num_audio_channels
											__a       =		hop_length
											__a       =		chunk_length
											__a       =		sampling_rate





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											return {
											    "spectrogram_length": self.spectrogram_length,
											    "feature_size": self.feature_size,
											    "num_audio_channels": self.num_audio_channels,
											    "hop_length": self.hop_length,
											    "chunk_length": self.chunk_length,
											    "sampling_rate": self.sampling_rate,
											}







							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		Any=False	):

											'''simple docstring'''
											def _flatten(__lowercase		:		Tuple	):
															return list(itertools.chain(*__lowercase	)	)

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






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Dict    =TvltFeatureExtractor





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		TvltFeatureExtractionTester(self	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)
											self.assertTrue(hasattr(__lowercase      ,				"""spectrogram_length"""	)	)
											self.assertTrue(hasattr(__lowercase      ,				"""feature_size"""	)	)
											self.assertTrue(hasattr(__lowercase      ,				"""num_audio_channels"""	)	)
											self.assertTrue(hasattr(__lowercase      ,				"""hop_length"""	)	)
											self.assertTrue(hasattr(__lowercase      ,				"""chunk_length"""	)	)
											self.assertTrue(hasattr(__lowercase      ,				"""sampling_rate"""	)	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

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

											__a       =		feat_extract_first.to_dict()
											__a       =		feat_extract_second.to_dict()
											__a       =		dict_first.pop("""mel_filters"""	)
											__a       =		dict_second.pop("""mel_filters"""	)
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase	)	)
											self.assertEqual(__lowercase      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											# Initialize feature_extractor
											__a       =		self.feature_extraction_class(**self.feat_extract_dict	)

											# create three inputs of length 800, 1000, and 1200
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		[np.asarray(__lowercase	) for speech_input in speech_inputs]

											# Test not batched input
											__a       =		feature_extractor(np_speech_inputs[0]      ,				return_tensors="""np"""      ,				sampling_rate=44100	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test batched
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test audio masking
											__a       =		feature_extractor(
											    __lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100      ,				mask_audio=__lowercase	).audio_values

											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

											# Test 2-D numpy arrays are batched.
											__a       =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											__a       =		np.asarray(__lowercase	)
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""      ,				sampling_rate=44100	).audio_values
											self.assertTrue(encoded_audios.ndim == 4	)
											self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
											self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
											self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		str	):

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

											return [x["array"] for x in speech_samples]







							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self._load_datasamples(1	)
											__a       =		TvltFeatureExtractor()
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""pt"""	).audio_values

											self.assertEquals(audio_values.shape      ,				(1, 1, 192, 128)	)

											__a       =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]	)
											self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,				__lowercase      ,				atol=1E-4	)	)
