






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


a						:			Union[str, Any]     	=					1e-4

if is_torch_available():
       import torch

       from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
       from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class        a       :



      """simple docstring"""


      def __init__( self   :     str						, __lowercase   :     Any						, __lowercase   :     Optional[int]=16						, __lowercase   :     Union[str, Any]=13						, __lowercase   :     int=7						, __lowercase   :     List[Any]=14						, __lowercase   :     List[Any]=10						, __lowercase   :     List[str]=19						, __lowercase   :     List[str]=5						, __lowercase   :     Optional[int]=4						, __lowercase   :     int=True						, __lowercase   :     Dict=16						, __lowercase   :     Any=2						, __lowercase   :     Union[str, Any]=4						, __lowercase   :     Optional[Any]=4						, __lowercase   :     Union[str, Any]="gelu"						, __lowercase   :     Optional[Any]=0.1						, __lowercase   :     Dict=0.1						, __lowercase   :     List[str]=[1, 2, 3, 4, 5]						, __lowercase   :     Optional[Any]=25						, __lowercase   :     List[str]=5						, )				-> Dict:
        __UpperCAmelCase				:      Any	 =    d_model
        __UpperCAmelCase				:      Dict	 =    parent
        __UpperCAmelCase				:      List[Any]	 =    batch_size
        __UpperCAmelCase				:      List[str]	 =    prediction_length
        __UpperCAmelCase				:      Dict	 =    context_length
        __UpperCAmelCase				:      Any	 =    cardinality
        __UpperCAmelCase				:      Any	 =    num_time_features
        __UpperCAmelCase				:      Optional[Any]	 =    lags_sequence
        __UpperCAmelCase				:      Union[str, Any]	 =    embedding_dimension
        __UpperCAmelCase				:      List[str]	 =    is_training
        __UpperCAmelCase				:      Optional[int]	 =    hidden_size
        __UpperCAmelCase				:      List[Any]	 =    num_hidden_layers
        __UpperCAmelCase				:      List[str]	 =    num_attention_heads
        __UpperCAmelCase				:      Optional[Any]	 =    intermediate_size
        __UpperCAmelCase				:      List[str]	 =    hidden_act
        __UpperCAmelCase				:      Any	 =    hidden_dropout_prob
        __UpperCAmelCase				:      List[str]	 =    attention_probs_dropout_prob

        __UpperCAmelCase				:      Any	 =    context_length
        __UpperCAmelCase				:      int	 =    prediction_length + label_length
        __UpperCAmelCase				:      List[str]	 =    label_length

        __UpperCAmelCase				:      Union[str, Any]	 =    moving_average
        __UpperCAmelCase				:      Optional[int]	 =    autocorrelation_factor
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[Any]:
        return AutoformerConfig(
            d_model=self.d_model						, encoder_layers=self.num_hidden_layers						, decoder_layers=self.num_hidden_layers						, encoder_attention_heads=self.num_attention_heads						, decoder_attention_heads=self.num_attention_heads						, encoder_ffn_dim=self.intermediate_size						, decoder_ffn_dim=self.intermediate_size						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, prediction_length=self.prediction_length						, context_length=self.context_length						, label_length=self.label_length						, lags_sequence=self.lags_sequence						, num_time_features=self.num_time_features						, num_static_categorical_features=1						, cardinality=[self.cardinality]						, embedding_dimension=[self.embedding_dimension]						, moving_average=self.moving_average						, )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     str   )				-> Optional[Any]:
        __UpperCAmelCase				:      Tuple	 =    config.context_length + max(config.lags_sequence   )

        __UpperCAmelCase				:      str	 =    ids_tensor([self.batch_size, 1]						, config.cardinality[0]   )
        __UpperCAmelCase				:      str	 =    floats_tensor([self.batch_size, _past_length, config.num_time_features]   )
        __UpperCAmelCase				:      Dict	 =    floats_tensor([self.batch_size, _past_length]   )
        __UpperCAmelCase				:      Dict	 =    floats_tensor([self.batch_size, _past_length]   ) > 0.5

        # decoder inputs
        __UpperCAmelCase				:      Optional[int]	 =    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]   )
        __UpperCAmelCase				:      Any	 =    floats_tensor([self.batch_size, config.prediction_length]   )

        __UpperCAmelCase				:      Union[str, Any]	 =    {
            """past_values""": past_values,
            """static_categorical_features""": static_categorical_features,
            """past_time_features""": past_time_features,
            """past_observed_mask""": past_observed_mask,
            """future_time_features""": future_time_features,
            """future_values""": future_values,
        }
        return inputs_dict
      def       UpperCAmelCase ( self   :     Dict   )				-> List[Any]:
        __UpperCAmelCase				:      Any	 =    self.get_config()
        __UpperCAmelCase				:      int	 =    self.prepare_autoformer_inputs_dict(__lowercase   )
        return config, inputs_dict
      def       UpperCAmelCase ( self   :     Any   )				-> Optional[int]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self.prepare_config_and_inputs()
        return config, inputs_dict




      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Union[str, Any]   )				-> int:
        __UpperCAmelCase				:      int	 =    AutoformerModel(config=__lowercase   ).to(__lowercase   ).eval()
        __UpperCAmelCase				:      int	 =    model(**__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    outputs.encoder_last_hidden_state
        __UpperCAmelCase				:      Tuple	 =    outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
          __UpperCAmelCase				:      int	 =    model.get_encoder()
          encoder.save_pretrained(__lowercase   )
          __UpperCAmelCase				:      List[str]	 =    AutoformerEncoder.from_pretrained(__lowercase   ).to(__lowercase   )

        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    model.create_network_inputs(**__lowercase   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]   )

        __UpperCAmelCase				:      Tuple	 =    torch.cat(
            (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])						, dim=-1						, )
        __UpperCAmelCase				:      str	 =    encoder(inputs_embeds=__lowercase   )[0]
        self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3   )

        __UpperCAmelCase				:      int	 =    (
            torch.mean(transformer_inputs[:, : config.context_length, ...]						, dim=1   )
            .unsqueeze(1   )
            .repeat(1						, config.prediction_length						, 1   )
        )
        __UpperCAmelCase				:      int	 =    torch.zeros(
            [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]						, device=enc_input.device						, )

        __UpperCAmelCase				:      int	 =    torch.cat(
            (
                torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)						, dim=1   ),
                feature[:, config.context_length - config.label_length :, ...],
            )						, dim=-1						, )
        __UpperCAmelCase				:      Dict	 =    torch.cat(
            (
                torch.cat((trend_input[:, -config.label_length :, ...], mean)						, dim=1   ),
                feature[:, config.context_length - config.label_length :, ...],
            )						, dim=-1						, )

        with tempfile.TemporaryDirectory() as tmpdirname:
          __UpperCAmelCase				:      Optional[Any]	 =    model.get_decoder()
          decoder.save_pretrained(__lowercase   )
          __UpperCAmelCase				:      List[Any]	 =    AutoformerDecoder.from_pretrained(__lowercase   ).to(__lowercase   )

        __UpperCAmelCase				:      Optional[int]	 =    decoder(
            trend=__lowercase						, inputs_embeds=__lowercase						, encoder_hidden_states=__lowercase						, )[0]

        self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3   )


@require_torch
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Dict							=      (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      a    :					Any							=      (AutoformerForPrediction,) if is_torch_available() else ()
      a    :					Dict							=      {'feature-extraction': AutoformerModel} if is_torch_available() else {}
      a    :					Optional[int]							=      False
      a    :					List[str]							=      False
      a    :					str							=      False
      a    :					Optional[int]							=      False
      a    :					Union[str, Any]							=      False
      a    :					Tuple							=      False
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> List[str]:
        __UpperCAmelCase				:      Tuple	 =    AutoformerModelTester(self   )
        __UpperCAmelCase				:      Dict	 =    ConfigTester(self						, config_class=__lowercase						, has_text_modality=__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> Tuple:
        self.config_tester.run_common_tests()
      def       UpperCAmelCase ( self   :     Dict   )				-> str:
        __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Any	 =    model_class(__lowercase   )

          with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__lowercase   )
            __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    model_class.from_pretrained(__lowercase						, output_loading_info=__lowercase   )
          self.assertEqual(info["""missing_keys"""]						, []   )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> int:
        __UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*__lowercase   )
      @unittest.skip(reason="""Model has no tokens embeddings"""   )
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        pass
      def       UpperCAmelCase ( self   :     Any   )				-> Optional[Any]:
        __UpperCAmelCase				:      Any	 =    inspect.signature(getattr(__lowercase						, """forward"""   )   )
        # The main input is the name of the argument after `self`
        __UpperCAmelCase				:      Optional[int]	 =    list(model_signature.parameters.keys()   )[1]
        self.assertEqual(AutoformerModel.main_input_name						, __lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Union[str, Any]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Tuple	 =    model_class(__lowercase   )
          __UpperCAmelCase				:      Any	 =    inspect.signature(model.forward   )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase				:      Tuple	 =    [*signature.parameters.keys()]

          __UpperCAmelCase				:      Tuple	 =    [
              """past_values""",
              """past_time_features""",
              """past_observed_mask""",
              """static_categorical_features""",
              """static_real_features""",
              """future_values""",
              """future_time_features""",
          ]

          if model.__class__.__name__ in ["AutoformerForPrediction"]:
            expected_arg_names.append("""future_observed_mask"""   )

          expected_arg_names.extend(
              [
                  """decoder_attention_mask""",
                  """head_mask""",
                  """decoder_head_mask""",
                  """cross_attn_head_mask""",
                  """encoder_outputs""",
                  """past_key_values""",
                  """output_hidden_states""",
                  """output_attentions""",
                  """use_cache""",
                  """return_dict""",
              ]   )

          self.assertListEqual(arg_names[: len(__lowercase   )]						, __lowercase   )
      def       UpperCAmelCase ( self   :     int   )				-> Optional[int]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase				:      Any	 =    True

        __UpperCAmelCase				:      List[str]	 =    getattr(self.model_tester						, """seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    getattr(self.model_tester						, """decoder_seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Dict	 =    getattr(self.model_tester						, """encoder_seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    getattr(self.model_tester						, """d_model"""						, __lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    getattr(self.model_tester						, """num_attention_heads"""						, __lowercase   )
        __UpperCAmelCase				:      List[Any]	 =    d_model // num_attention_heads

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Dict	 =    True
          __UpperCAmelCase				:      str	 =    False
          __UpperCAmelCase				:      Optional[Any]	 =    True
          __UpperCAmelCase				:      Union[str, Any]	 =    model_class(__lowercase   )
          model.to(__lowercase   )
          model.eval()
          with torch.no_grad():
            __UpperCAmelCase				:      int	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )   )
          __UpperCAmelCase				:      List[Any]	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_hidden_layers   )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __UpperCAmelCase				:      int	 =    True
          __UpperCAmelCase				:      int	 =    model_class(__lowercase   )
          model.to(__lowercase   )
          model.eval()
          with torch.no_grad():
            __UpperCAmelCase				:      List[str]	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )   )
          __UpperCAmelCase				:      Optional[int]	 =    outputs.encoder_attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_hidden_layers   )

          self.assertListEqual(
              list(attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, dim]						, )
          __UpperCAmelCase				:      List[str]	 =    len(__lowercase   )

          __UpperCAmelCase				:      Union[str, Any]	 =    7

          if "last_hidden_state" in outputs:
            correct_outlen += 1

          if "trend" in outputs:
            correct_outlen += 1

          if "past_key_values" in outputs:
            correct_outlen += 1  # past_key_values have been returned

          if "loss" in outputs:
            correct_outlen += 1

          if "params" in outputs:
            correct_outlen += 1

          self.assertEqual(__lowercase						, __lowercase   )

          # decoder attentions
          __UpperCAmelCase				:      List[Any]	 =    outputs.decoder_attentions
          self.assertIsInstance(__lowercase						, (list, tuple)   )
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_hidden_layers   )
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, decoder_seq_length, dim]						, )

          # cross attentions
          __UpperCAmelCase				:      Tuple	 =    outputs.cross_attentions
          self.assertIsInstance(__lowercase						, (list, tuple)   )
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_hidden_layers   )
          self.assertListEqual(
              list(cross_attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, decoder_seq_length, dim]						, )

        # Check attention is always last and order is fine
        __UpperCAmelCase				:      List[Any]	 =    True
        __UpperCAmelCase				:      int	 =    True
        __UpperCAmelCase				:      List[str]	 =    model_class(__lowercase   )
        model.to(__lowercase   )
        model.eval()
        with torch.no_grad():
          __UpperCAmelCase				:      int	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )   )

        self.assertEqual(out_len + 2						, len(__lowercase   )   )

        __UpperCAmelCase				:      Dict	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

        self.assertEqual(len(__lowercase   )						, self.model_tester.num_hidden_layers   )
        self.assertListEqual(
            list(self_attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, dim]						, )




      @is_flaky()
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[int]:
        super().test_retain_grad_hidden_states_attentions()






def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple="train-batch.pt"						):
  __UpperCAmelCase				:      str	 =    hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch"""    ,						filename=__lowerCamelCase    ,						repo_type="""dataset"""						)
  __UpperCAmelCase				:      Optional[Any]	 =    torch.load(__lowerCamelCase    ,						map_location=__lowerCamelCase						)
  return batch







@require_torch
@slow
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[Any]   )				-> List[Any]:
        __UpperCAmelCase				:      Dict	 =    AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""   ).to(__lowercase   )
        __UpperCAmelCase				:      Dict	 =    prepare_batch()

        with torch.no_grad():
          __UpperCAmelCase				:      int	 =    model(
              past_values=batch["""past_values"""]						, past_time_features=batch["""past_time_features"""]						, past_observed_mask=batch["""past_observed_mask"""]						, static_categorical_features=batch["""static_categorical_features"""]						, future_values=batch["""future_values"""]						, future_time_features=batch["""future_time_features"""]						, )[0]

        __UpperCAmelCase				:      Dict	 =    torch.Size(
            (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)   )
        self.assertEqual(output.shape						, __lowercase   )

        __UpperCAmelCase				:      Optional[int]	 =    torch.tensor(
            [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]						, device=__lowercase   )
        self.assertTrue(torch.allclose(output[0, :3, :3]						, __lowercase						, atol=__lowercase   )   )
      def       UpperCAmelCase ( self   :     str   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Union[str, Any]	 =    AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""   ).to(__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    prepare_batch("""val-batch.pt"""   )
        with torch.no_grad():
          __UpperCAmelCase				:      Any	 =    model(
              past_values=batch["""past_values"""]						, past_time_features=batch["""past_time_features"""]						, past_observed_mask=batch["""past_observed_mask"""]						, static_categorical_features=batch["""static_categorical_features"""]						, ).encoder_last_hidden_state
        __UpperCAmelCase				:      Optional[int]	 =    torch.Size((64, model.config.context_length, model.config.d_model)   )
        self.assertEqual(output.shape						, __lowercase   )

        __UpperCAmelCase				:      Dict	 =    torch.tensor(
            [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]						, device=__lowercase   )
        self.assertTrue(torch.allclose(output[0, :3, :3]						, __lowercase						, atol=__lowercase   )   )




      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[int]:
        __UpperCAmelCase				:      Union[str, Any]	 =    AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly"""   ).to(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    prepare_batch("""val-batch.pt"""   )
        with torch.no_grad():
          __UpperCAmelCase				:      int	 =    model.generate(
              static_categorical_features=batch["""static_categorical_features"""]						, past_time_features=batch["""past_time_features"""]						, past_values=batch["""past_values"""]						, future_time_features=batch["""future_time_features"""]						, past_observed_mask=batch["""past_observed_mask"""]						, )
        __UpperCAmelCase				:      Optional[Any]	 =    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)   )
        self.assertEqual(outputs.sequences.shape						, __lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]						, device=__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    outputs.sequences.mean(dim=1   )
        self.assertTrue(torch.allclose(mean_prediction[0, -3:]						, __lowercase						, rtol=1e-1   )   )




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

import numpy as np


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					np.array:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		_UpperCamelCase         =						"""f32le"""
		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-i""",
		    """pipe:0""",
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "f32le"    ,	)		->					Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		_UpperCamelCase         =						platform.system()
		if system == "Linux":
				_UpperCamelCase         =						"""alsa"""
				_UpperCamelCase         =						"""default"""
		elif system == "Darwin":
				_UpperCamelCase         =						"""avfoundation"""
				_UpperCamelCase         =						""":0"""
		elif system == "Windows":
				_UpperCamelCase         =						"""dshow"""
				_UpperCamelCase         =						"""default"""

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


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = "f32le"    ,	)		->					Any:

		"""simple docstring"""






		if stream_chunk_s is not None:
				_UpperCamelCase         =						stream_chunk_s
		else:
				_UpperCamelCase         =						chunk_length_s

		_UpperCamelCase         =						ffmpeg_microphone(lowerCAmelCase    ,	lowerCAmelCase    ,	format_for_conversion=lowerCAmelCase			)
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						np.intaa
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						np.floataa
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		if stride_length_s is None:
				_UpperCamelCase         =						chunk_length_s / 6
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		if isinstance(lowerCAmelCase    ,	(int, float)			):
				_UpperCamelCase         =						[stride_length_s, stride_length_s]

		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
		_UpperCamelCase         =						datetime.datetime.now()
		_UpperCamelCase         =						datetime.timedelta(seconds=lowerCAmelCase			)
		for item in chunk_bytes_iter(lowerCAmelCase    ,	lowerCAmelCase    ,	stride=(stride_left, stride_right)    ,	stream=lowerCAmelCase			):
				# Put everything back in numpy scale
				_UpperCamelCase         =						np.frombuffer(item["""raw"""]    ,	dtype=lowerCAmelCase			)
				_UpperCamelCase         =						(
				    item["""stride"""][0] // size_of_sample,
				    item["""stride"""][1] // size_of_sample,
				)
				_UpperCamelCase         =						sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 1_0 * delta:
						# We're late !! SKIP
						continue
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False			)		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						b""""""
		_UpperCamelCase     ,						_UpperCamelCase         =						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}'			)
		_UpperCamelCase         =						0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase			) < chunk_len:
						_UpperCamelCase         =						(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase			) >= chunk_len:
								# We are flushing the accumulator
								_UpperCamelCase         =						(_stride_left, stride_right)
								_UpperCamelCase         =						{"""raw""": acc[:chunk_len], """stride""": stride}
								if stream:
										_UpperCamelCase         =						False
								yield item
								_UpperCamelCase         =						stride_left
								_UpperCamelCase         =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase			) > stride_left:
				_UpperCamelCase         =						{"""raw""": acc, """stride""": (_stride_left, 0)}
				if stream:
						_UpperCamelCase         =						False
				yield item







def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						2**2_4  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdout=subprocess.PIPE    ,	bufsize=lowerCAmelCase			) as ffmpeg_process:
						while True:
								_UpperCamelCase         =						ffmpeg_process.stdout.read(lowerCAmelCase			)
								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



