



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					MvpTokenizer
							UpperCamelCase__				:      Tuple         =					MvpTokenizerFast
							UpperCamelCase__				:      Union[str, Any]         =					True
							UpperCamelCase__				:      Dict         =					filter_roberta_detectors


							def _a      (							self		):
											super().setUp()
											a__				      =		[
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											a__				      =		dict(zip(a_  ,  range(len(a_		)		)		)		)
											a__				      =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											a__				      =		{"""unk_token""": """<unk>"""}

											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
											a__				      =		os.path.join(self.tmpdirname  ,  VOCAB_FILES_NAMES["""merges_file"""]		)
											with open(self.vocab_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write(json.dumps(a_		) + """\n"""		)
											with open(self.merges_file  ,  """w"""  ,  encoding="""utf-8"""		) as fp:
															fp.write("""\n""".join(a_		)		)


							def _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  **a_		):
											kwargs.update(self.special_tokens_map		)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,  **a_		)


							def _a      (							self  ,  a_		):
											return "lower newer", "lower newer"


							@cached_property
							def _a      (							self		):
											return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""		)


							@cached_property
							def _a      (							self		):
											return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""		)


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											a__				      =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  max_length=len(a_		)  ,  padding=a_  ,  return_tensors="""pt"""		)
															self.assertIsInstance(a_  ,  a_		)

															self.assertEqual((2, 9)  ,  batch.input_ids.shape		)
															self.assertEqual((2, 9)  ,  batch.attention_mask.shape		)
															a__				      =		batch.input_ids.tolist()[0]
															self.assertListEqual(a_  ,  a_		)
															# Test that special tokens are reset


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  padding=a_  ,  return_tensors="""pt"""		)
															# check if input_ids are returned and no labels
															self.assertIn("""input_ids"""  ,  a_		)
															self.assertIn("""attention_mask"""  ,  a_		)
															self.assertNotIn("""labels"""  ,  a_		)
															self.assertNotIn("""decoder_attention_mask"""  ,  a_		)


							@require_torch
							def _a      (							self		):
											a__				      =		[
											    """Summary of the text.""",
											    """Another summary.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(text_target=a_  ,  max_length=32  ,  padding="""max_length"""  ,  return_tensors="""pt"""		)
															self.assertEqual(32  ,  targets["""input_ids"""].shape[1]		)


							@require_torch
							def _a      (							self		):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(
															    ["""I am a small frog""" * 1_024, """I am a small frog"""]  ,  padding=a_  ,  truncation=a_  ,  return_tensors="""pt"""		)
															self.assertIsInstance(a_  ,  a_		)
															self.assertEqual(batch.input_ids.shape  ,  (2, 1_024)		)


							@require_torch
							def _a      (							self		):
											a__				      =		["""A long paragraph for summarization."""]
											a__				      =		[
											    """Summary of the text.""",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a__				      =		tokenizer(a_  ,  text_target=a_  ,  return_tensors="""pt"""		)
															a__				      =		inputs["""input_ids"""]
															a__				      =		inputs["""labels"""]
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)


							def _a      (							self		):
											pass







							def _a      (							self		):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																			a__				      =		self.rust_tokenizer_class.from_pretrained(a_  ,  **a_		)
																			a__				      =		self.tokenizer_class.from_pretrained(a_  ,  **a_		)
																			a__				      =		"""A, <mask> AllenNLP sentence."""
																			a__				      =		tokenizer_r.encode_plus(a_  ,  add_special_tokens=a_  ,  return_token_type_ids=a_		)
																			a__				      =		tokenizer_p.encode_plus(a_  ,  add_special_tokens=a_  ,  return_token_type_ids=a_		)

																			# token_type_ids should put 0 everywhere
																			self.assertEqual(sum(tokens_r["""token_type_ids"""]		)  ,  sum(tokens_p["""token_type_ids"""]		)		)

																			# attention_mask should put 1 everywhere, so sum over length should be 1
																			self.assertEqual(
																			    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)  ,  sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)  ,  )

																			a__				      =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
																			a__				      =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)

																			# Rust correctly handles the space before the mask while python doesnt
																			self.assertSequenceEqual(tokens_p["""input_ids"""]  ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
																			self.assertSequenceEqual(tokens_r["""input_ids"""]  ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

																			self.assertSequenceEqual(
																			    a_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
																			self.assertSequenceEqual(
																			    a_  ,  ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
	from .features import FeatureType


UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					False, False, False

@dataclass
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Optional[int]         =					None
							UpperCamelCase__				:      bool         =					True
							UpperCamelCase__				:      bool         =					True
							UpperCamelCase__				:      Optional[str]         =					None
							# Automatically constructed
							UpperCamelCase__				:      ClassVar[str]         =					"dict"
							UpperCamelCase__				:      ClassVar[Any]         =					pa.struct({"""bytes""": pa.binary(), """path""": pa.string()})
							UpperCamelCase__				:      str         =					field(default="""Audio"""			,init=SCREAMING_SNAKE_CASE			,repr=SCREAMING_SNAKE_CASE)


							def __call__(							self		):
											return self.pa_type


							def _a      (							self  ,  a_		):
											try:
															import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
											except ImportError as err:
															raise ImportError("""To support encoding audio data, please install 'soundfile'."""		) from err
											if isinstance(a_  ,  a_		):
															return {"bytes": None, "path": value}
											elif isinstance(a_  ,  a_		):
															return {"bytes": value, "path": None}
											elif "array" in value:
															# convert the audio array to wav bytes
															a__				      =		BytesIO()
															sf.write(a_  ,  value["""array"""]  ,  value["""sampling_rate"""]  ,  format="""wav"""		)
															return {"bytes": buffer.getvalue(), "path": None}
											elif value.get("""path"""		) is not None and os.path.isfile(value["""path"""]		):
															# we set "bytes": None to not duplicate the data if they're already available locally
															if value["path"].endswith("""pcm"""		):
																			# "PCM" only has raw audio bytes
																			if value.get("""sampling_rate"""		) is None:
																							# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																							raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""		)
																			if value.get("""bytes"""		):
																							# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																							a__				      =		np.frombuffer(value["""bytes"""]  ,  dtype=np.intaa		).astype(np.floataa		) / 32_767
																			else:
																							a__				      =		np.memmap(value["""path"""]  ,  dtype="""h"""  ,  mode="""r"""		).astype(np.floataa		) / 32_767

																			a__				      =		BytesIO(bytes()		)
																			sf.write(a_  ,  a_  ,  value["""sampling_rate"""]  ,  format="""wav"""		)
																			return {"bytes": buffer.getvalue(), "path": None}
															else:
																			return {"bytes": None, "path": value.get("""path"""		)}
											elif value.get("""bytes"""		) is not None or value.get("""path"""		) is not None:
															# store the audio bytes, and path is used to infer the audio format using the file extension
															return {"bytes": value.get("""bytes"""		), "path": value.get("""path"""		)}
											else:
															raise ValueError(
															    F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											if not self.decode:
															raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""		)

											a__      ,			a__				      =		(value["""path"""], BytesIO(value["""bytes"""]		)) if value["""bytes"""] is not None else (value["""path"""], None)
											if path is None and file is None:
															raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''		)

											try:
															import librosa
															import soundfile as sf
											except ImportError as err:
															raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""		) from err

											a__				      =		xsplitext(a_		)[1][1:].lower() if path is not None else None
											if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
															raise RuntimeError(
															    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
															    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """		)
											elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
															raise RuntimeError(
															    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
															    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """		)

											if file is None:
															a__				      =		token_per_repo_id or {}
															a__				      =		path.split("""::"""		)[-1]
															try:
																			a__				      =		string_to_dict(a_  ,  config.HUB_DATASETS_URL		)["""repo_id"""]
																			a__				      =		token_per_repo_id[repo_id]
															except (ValueError, KeyError):
																			a__				      =		None

															with xopen(a_  ,  """rb"""  ,  use_auth_token=a_		) as f:
																			a__      ,			a__				      =		sf.read(a_		)

											else:
															a__      ,			a__				      =		sf.read(a_		)

											a__				      =		array.T
											if self.mono:
															a__				      =		librosa.to_mono(a_		)
											if self.sampling_rate and self.sampling_rate != sampling_rate:
															a__				      =		librosa.resample(a_  ,  orig_sr=a_  ,  target_sr=self.sampling_rate		)
															a__				      =		self.sampling_rate

											return {"path": path, "array": array, "sampling_rate": sampling_rate}


							def _a      (							self		):
											from .features import Value

											if self.decode:
															raise ValueError("""Cannot flatten a decoded Audio feature."""		)
											return {
											    "bytes": Value("""binary"""		),
											    "path": Value("""string"""		),
											}


							def _a      (							self  ,  a_		):
											if pa.types.is_string(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, storage]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											elif pa.types.is_binary(storage.type		):
															a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		pa.StructArray.from_arrays([storage, path_array]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											elif pa.types.is_struct(storage.type		) and storage.type.get_all_field_indices("""array"""		):
															a__				      =		pa.array([Audio().encode_example(a_		) if x is not None else None for x in storage.to_pylist()]		)
											elif pa.types.is_struct(storage.type		):
															if storage.type.get_field_index("""bytes"""		) >= 0:
																			a__				      =		storage.field("""bytes"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.binary()		)
															if storage.type.get_field_index("""path"""		) >= 0:
																			a__				      =		storage.field("""path"""		)
															else:
																			a__				      =		pa.array([None] * len(a_		)  ,  type=pa.string()		)
															a__				      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,  ["""bytes""", """path"""]  ,  mask=storage.is_null()		)
											return array_cast(a_  ,  self.pa_type		)







							def _a      (							self  ,  a_		):

											@no_op_if_value_is_null
											def path_to_bytes(a_		):
															with xopen(a_  ,  """rb"""		) as f:
																			a__				      =		f.read()
															return bytes_

											a__				      =		pa.array(
											    [
											        (path_to_bytes(x["""path"""]		) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
											        for x in storage.to_pylist()
											    ]  ,  type=pa.binary()  ,  )
											a__				      =		pa.array(
											    [os.path.basename(a_		) if path is not None else None for path in storage.field("""path"""		).to_pylist()]  ,  type=pa.string()  ,  )
											a__				      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,  ["""bytes""", """path"""]  ,  mask=bytes_array.is_null()		)
											return array_cast(a_  ,  self.pa_type		)

