





"""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 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




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




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   List[Any]       =ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size      )

											__lowerCamelCase   :   str       =None
											if self.use_attention_mask:
																		__lowerCamelCase   :   Optional[Any]       =random_attention_mask([self.batch_size, self.seq_length]      )

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

											__lowerCamelCase   :   Union[str, 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=__lowercase    ,     initializer_range=self.initializer_range    ,     )

											return config, input_ids, token_type_ids, attention_mask




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Union[str, Any]       =self.prepare_config_and_inputs()
											__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =config_and_inputs
											__lowerCamelCase   :   List[Any]       ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict

@require_flax
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Union[str, Any]									=		True

				__snake_case :							Union[str, Any]									=		(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Optional[Any]       =FlaxRoFormerModelTester(self      )




				@slow
				def        __lowercase	(  self	:Optional[int]      ):
											for model_class_name in self.all_model_classes:
																		__lowerCamelCase   :   str       =model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,     from_pt=__lowercase      )
																		__lowerCamelCase   :   str       =model(np.ones((1, 1)      )      )
																		self.assertIsNotNone(__lowercase      )

@require_flax
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase   :   Union[str, Any]       =FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''      )
											__lowerCamelCase   :   Tuple       =jnp.array([[0, 1, 2, 3, 4, 5]]      )
											__lowerCamelCase   :   str       =model(__lowercase      )[0]

											__lowerCamelCase   :   Optional[Any]       =5_0000

											__lowerCamelCase   :   List[Any]       =(1, 6, vocab_size)
											self.assertEqual(output.shape    ,     __lowercase      )

											__lowerCamelCase   :   str       =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]    ,     __lowercase    ,     atol=1e-4      )      )











"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdin=subprocess.PIPE     ,    stdout=subprocess.PIPE ) as ffmpeg_process:
																					__lowerCamelCase   :   Union[str, Any]       =ffmpeg_process.communicate(SCREAMING_SNAKE_CASE )
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
							__lowerCamelCase   :   Any       =output_stream[0]
							__lowerCamelCase   :   Tuple       =np.frombuffer(SCREAMING_SNAKE_CASE     ,    np.floataa )
							if audio.shape[0] == 0:
														raise ValueError('''Malformed soundfile''' )
							return audio



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   List[Any]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   List[Any]       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

							__lowerCamelCase   :   Any       =[
							    '''ffmpeg''',
							    '''-f''',
							    format_,
							    '''-i''',
							    input_,
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-fflags''',
							    '''nobuffer''',
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]
							__lowerCamelCase   :   Tuple       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							__lowerCamelCase   :   int       =_ffmpeg_stream(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							for item in iterator:
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

							__lowerCamelCase   :   Union[str, Any]       =ffmpeg_microphone(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    format_for_conversion=SCREAMING_SNAKE_CASE )
							if format_for_conversion == "s16le":
														__lowerCamelCase   :   int       =np.intaa
														__lowerCamelCase   :   Optional[int]       =2
							elif format_for_conversion == "f32le":
														__lowerCamelCase   :   Dict       =np.floataa
														__lowerCamelCase   :   Tuple       =4
							else:
														raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

							__lowerCamelCase   :   List[str]       =int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
							__lowerCamelCase   :   int       =int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
							__lowerCamelCase   :   Optional[int]       =datetime.datetime.now()
							__lowerCamelCase   :   List[Any]       =datetime.timedelta(seconds=SCREAMING_SNAKE_CASE )
							for item in chunk_bytes_iter(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    stride=(stride_left, stride_right)     ,    stream=SCREAMING_SNAKE_CASE ):
														# Put everything back in numpy scale
														__lowerCamelCase   :   Optional[int]       =np.frombuffer(item['''raw''']     ,    dtype=SCREAMING_SNAKE_CASE )
														__lowerCamelCase   :   Dict       =(
														    item['''stride'''][0] // size_of_sample,
														    item['''stride'''][1] // size_of_sample,
														)
														__lowerCamelCase   :   Optional[Any]       =sampling_rate
														audio_time += delta
														if datetime.datetime.now() > audio_time + 10 * delta:
																					# We're late !! SKIP
																					continue
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       =B''''''
							__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =stride
							if stride_left + stride_right >= chunk_len:
														raise ValueError(
														    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
							__lowerCamelCase   :   List[str]       =0
							for raw in iterator:
														acc += raw
														if stream and len(SCREAMING_SNAKE_CASE ) < chunk_len:
																					__lowerCamelCase   :   List[str]       =(_stride_left, 0)
																					yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
														else:
																					while len(SCREAMING_SNAKE_CASE ) >= chunk_len:
																												# We are flushing the accumulator
																												__lowerCamelCase   :   Optional[Any]       =(_stride_left, stride_right)
																												__lowerCamelCase   :   str       ={'''raw''': acc[:chunk_len], '''stride''': stride}
																												if stream:
																																			__lowerCamelCase   :   Optional[Any]       =False
																												yield item
																												__lowerCamelCase   :   Dict       =stride_left
																												__lowerCamelCase   :   str       =acc[chunk_len - stride_left - stride_right :]
    # Last chunk
							if len(SCREAMING_SNAKE_CASE ) > stride_left:
														__lowerCamelCase   :   List[str]       ={'''raw''': acc, '''stride''': (_stride_left, 0)}
														if stream:
																					__lowerCamelCase   :   Tuple       =False
														yield item



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												if raw == b"":
																																			break
																												yield raw
							except FileNotFoundError as error:
														raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error




