"""simple docstring"""







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      _lowercase						(				__lowerCamelCase , unittest.TestCase					):
				lowerCamelCase__									=    GPTNeoXJapaneseTokenizer
				lowerCamelCase__									=    False
				lowerCamelCase__									=    {'do_clean_text': False, 'add_prefix_space': False}

				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										super().setUp()

										UpperCAmelCase__   		=						[
										    """こん""",
										    """こんに""",
										    """にちは""",
										    """ばんは""",
										    """世界,㔺界""",
										    """、""",
										    """。""",
										    """<BR>""",
										    """<SP>""",
										    """<TAB>""",
										    """<URL>""",
										    """<EMAIL>""",
										    """<TEL>""",
										    """<DATE>""",
										    """<PRICE>""",
										    """<BLOCK>""",
										    """<KIGOU>""",
										    """<U2000U2BFF>""",
										    """<|emoji1|>""",
										    """<unk>""",
										    """<|startoftext|>""",
										    """<|endoftext|>""",
										]
										UpperCAmelCase__   		=						{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
										UpperCAmelCase__   		=						{"""unk_token""": """<unk>"""}

										UpperCAmelCase__   		=						os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES["""vocab_file"""]       )
										UpperCAmelCase__   		=						os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES["""emoji_file"""]       )
										with open(self.vocab_file    ,      """w"""    ,      encoding="""utf-8"""       ) as vocab_writer:
																vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )
										with open(self.emoji_file    ,      """w"""       ) as emoji_writer:
																emoji_writer.write(json.dumps(_lowercase       )       )

				def     A	(       self    :      Any    ,      **_lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										kwargs.update(self.special_tokens_map       )
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname    ,      **_lowercase       )

				def     A	(       self    :      str    ,      _lowercase    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
										UpperCAmelCase__   		=						"""こんにちは、世界。 \nこんばんは、世界。😀"""
										return input_text, output_text

				def     A	(       self    :      List[Any]    ,      _lowercase    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__		, UpperCAmelCase__   		=						self.get_input_output_texts(_lowercase       )
										UpperCAmelCase__   		=						tokenizer.encode(_lowercase    ,      add_special_tokens=_lowercase       )
										UpperCAmelCase__   		=						tokenizer.decode(_lowercase    ,      clean_up_tokenization_spaces=_lowercase       )
										return text, ids

				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										pass  # TODO add if relevant

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										pass  # TODO add if relevant

				def     A	(       self    :      str       ):


										'''simple docstring'''
										pass  # TODO add if relevant

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.get_tokenizer()

										# Testing tokenization
										UpperCAmelCase__   		=						"""こんにちは、世界。　こんばんは、㔺界。"""
										UpperCAmelCase__   		=						["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
										UpperCAmelCase__   		=						tokenizer.tokenize(_lowercase       )
										self.assertListEqual(_lowercase    ,      _lowercase       )

										# Testing conversion to ids without special tokens
										UpperCAmelCase__   		=						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
										UpperCAmelCase__   		=						tokenizer.convert_tokens_to_ids(_lowercase       )
										self.assertListEqual(_lowercase    ,      _lowercase       )

										# Testing conversion to ids with special tokens
										UpperCAmelCase__   		=						tokens + [tokenizer.unk_token]
										UpperCAmelCase__   		=						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
										UpperCAmelCase__   		=						tokenizer.convert_tokens_to_ids(_lowercase       )
										self.assertListEqual(_lowercase    ,      _lowercase       )

				@slow
				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""       )

										UpperCAmelCase__   		=						tokenizer.encode("""ありがとう。"""    ,      add_special_tokens=_lowercase       )
										UpperCAmelCase__   		=						tokenizer.encode("""どういたしまして。"""    ,      add_special_tokens=_lowercase       )

										UpperCAmelCase__   		=						tokenizer.build_inputs_with_special_tokens(_lowercase       )
										UpperCAmelCase__   		=						tokenizer.build_inputs_with_special_tokens(_lowercase    ,      _lowercase       )

										assert encoded_sentence == ids_a
										assert encoded_pair == ids_a + ids_a

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										# Intentionally convert some words to accommodate character fluctuations unique to Japanese
										pass

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										# tokenizer has no padding token
										pass


"""simple docstring"""







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
						)


from PIL import Image  # noqa: E402


class      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

										# Pad with silence if necessary.
										if len(self.audio       ) < self.x_res * self.hop_length:
																UpperCAmelCase__   		=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

