


'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)


class 						_snake_case   (       unittest.TestCase		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=5							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=16							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=4							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   str       				=       batch_size
								UpperCAmelCase__					:   Tuple       				=       seq_length
								UpperCAmelCase__					:   Any       				=       is_training
								UpperCAmelCase__					:   Tuple       				=       use_attention_mask
								UpperCAmelCase__					:   Optional[Any]       				=       use_token_type_ids
								UpperCAmelCase__					:   Optional[Any]       				=       use_labels
								UpperCAmelCase__					:   str       				=       vocab_size
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   int       				=       num_hidden_layers
								UpperCAmelCase__					:   Optional[int]       				=       num_attention_heads
								UpperCAmelCase__					:   Optional[int]       				=       intermediate_size
								UpperCAmelCase__					:   Tuple       				=       hidden_act
								UpperCAmelCase__					:   Optional[Any]       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Union[str, Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[Any]       				=       max_position_embeddings
								UpperCAmelCase__					:   Optional[int]       				=       type_vocab_size
								UpperCAmelCase__					:   str       				=       type_sequence_label_size
								UpperCAmelCase__					:   Optional[int]       				=       initializer_range
								UpperCAmelCase__					:   str       				=       num_choices




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Union[str, Any]       				=       None
								if self.use_attention_mask:
											UpperCAmelCase__					:   Tuple       				=       random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase__					:   Dict       				=       None
								if self.use_token_type_ids:
											UpperCAmelCase__					:   Any       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

								UpperCAmelCase__					:   Optional[Any]       				=       RoFormerConfig(
								    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							is_decoder=_lowerCamelCase							,							initializer_range=self.initializer_range							,							)

								return config, input_ids, token_type_ids, attention_mask




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__					:   Optional[Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Dict       				=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict


@require_flax
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Optional[int]						=   True

					lowerCAmelCase :Any						=   (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       FlaxRoFormerModelTester(self)




					@slow
					def 			snake_case__  ( self):
								for model_class_name in self.all_model_classes:
											UpperCAmelCase__					:   List[str]       				=       model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,							from_pt=_lowerCamelCase)
											UpperCAmelCase__					:   Dict       				=       model(np.ones((1, 1)))
											self.assertIsNotNone(_lowerCamelCase)


@require_flax
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
								UpperCAmelCase__					:   int       				=       jnp.array([[0, 1, 2, 3, 4, 5]])
								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase)[0]

								UpperCAmelCase__					:   Union[str, Any]       				=       5_0000

								UpperCAmelCase__					:   Any       				=       (1, 6, vocab_size)
								self.assertEqual(output.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,							_lowerCamelCase							,							atol=1e-4))



'''simple docstring'''



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


__A		,	__A		,	__A		=False, False, False


@dataclass
class 						_snake_case   :
					lowerCAmelCase :Optional[int]						=   None
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :bool						=   True
					lowerCAmelCase :Optional[str]						=   None
					# Automatically constructed
					lowerCAmelCase :ClassVar[str]						=   "dict"
					lowerCAmelCase :ClassVar[Any]						=   pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}		)
					lowerCAmelCase :str						=   field(default='''Audio'''    ,				init=a__    ,				repr=a__		)




					def __call__( self):
								return self.pa_type




					def 			snake_case__  ( self							,							_lowerCamelCase):
								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(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": None, "path": value}
								elif isinstance(_lowerCamelCase							,							_lowerCamelCase):
											return {"bytes": value, "path": None}
								elif "array" in value:
											# convert the audio array to wav bytes
											UpperCAmelCase__					:   Optional[int]       				=       BytesIO()
											sf.write(_lowerCamelCase							,							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!)
																	UpperCAmelCase__					:   Tuple       				=       np.frombuffer(value["""bytes"""]							,							dtype=np.intaa).astype(np.floataa) / 3_2767
														else:
																	UpperCAmelCase__					:   List[str]       				=       np.memmap(value["""path"""]							,							dtype="""h"""							,							mode="""r""").astype(np.floataa) / 3_2767

														UpperCAmelCase__					:   List[str]       				=       BytesIO(bytes())
														sf.write(_lowerCamelCase							,							_lowerCamelCase							,							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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""")

								UpperCAmelCase__				,    UpperCAmelCase__					:   Tuple       				=       (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

								UpperCAmelCase__					:   Dict       				=       xsplitext(_lowerCamelCase)[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:
											UpperCAmelCase__					:   int       				=       token_per_repo_id or {}
											UpperCAmelCase__					:   Optional[int]       				=       path.split("""::""")[-1]
											try:
														UpperCAmelCase__					:   Dict       				=       string_to_dict(_lowerCamelCase							,							config.HUB_DATASETS_URL)["""repo_id"""]
														UpperCAmelCase__					:   Dict       				=       token_per_repo_id[repo_id]
											except (ValueError, KeyError):
														UpperCAmelCase__					:   List[Any]       				=       None

											with xopen(_lowerCamelCase							,							"""rb"""							,							use_auth_token=_lowerCamelCase) as f:
														UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       sf.read(_lowerCamelCase)

								else:
											UpperCAmelCase__				,    UpperCAmelCase__					:   List[Any]       				=       sf.read(_lowerCamelCase)

								UpperCAmelCase__					:   str       				=       array.T
								if self.mono:
											UpperCAmelCase__					:   List[Any]       				=       librosa.to_mono(_lowerCamelCase)
								if self.sampling_rate and self.sampling_rate != sampling_rate:
											UpperCAmelCase__					:   int       				=       librosa.resample(_lowerCamelCase							,							orig_sr=_lowerCamelCase							,							target_sr=self.sampling_rate)
											UpperCAmelCase__					:   Tuple       				=       self.sampling_rate

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




					def 			snake_case__  ( self):
								from .features import Value

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




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if pa.types.is_string(storage.type):
											UpperCAmelCase__					:   Dict       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											UpperCAmelCase__					:   Tuple       				=       pa.StructArray.from_arrays([bytes_array, storage]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								elif pa.types.is_binary(storage.type):
											UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   str       				=       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"""):
											UpperCAmelCase__					:   Optional[Any]       				=       pa.array([Audio().encode_example(_lowerCamelCase) 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:
														UpperCAmelCase__					:   int       				=       storage.field("""bytes""")
											else:
														UpperCAmelCase__					:   List[str]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.binary())
											if storage.type.get_field_index("""path""") >= 0:
														UpperCAmelCase__					:   List[Any]       				=       storage.field("""path""")
											else:
														UpperCAmelCase__					:   Optional[int]       				=       pa.array([None] * len(_lowerCamelCase)							,							type=pa.string())
											UpperCAmelCase__					:   Optional[int]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=storage.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)




					def 			snake_case__  ( self							,							_lowerCamelCase):

								@no_op_if_value_is_null
								def path_to_bytes(_lowerCamelCase):
											with xopen(_lowerCamelCase							,							"""rb""") as f:
														UpperCAmelCase__					:   int       				=       f.read()
											return bytes_

								UpperCAmelCase__					:   Optional[Any]       				=       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()							,							)
								UpperCAmelCase__					:   Optional[Any]       				=       pa.array(
								    [os.path.basename(_lowerCamelCase) if path is not None else None for path in storage.field("""path""").to_pylist()]							,							type=pa.string()							,							)
								UpperCAmelCase__					:   List[str]       				=       pa.StructArray.from_arrays([bytes_array, path_array]							,							["""bytes""", """path"""]							,							mask=bytes_array.is_null())
								return array_cast(_lowerCamelCase							,							self.pa_type)