"""simple docstring"""





import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"encodec"





				def __init__(      self							:							Union[str, Any]      ,    _A							:							int=[1.5, 3.0, 6.0, 12.0, 24.0]      ,    _A							:							Any=2_4_0_0_0      ,    _A							:							Dict=1      ,    _A							:							Union[str, Any]=False      ,    _A							:							Optional[Any]=None      ,    _A							:							Optional[int]=None      ,    _A							:							List[Any]=1_2_8      ,    _A							:							Union[str, Any]=3_2      ,    _A							:							Tuple=1      ,    _A							:							int=[8, 5, 4, 2]      ,    _A							:							List[Any]="weight_norm"      ,    _A							:							Any=7      ,    _A							:							Any=7      ,    _A							:							Any=3      ,    _A							:							Dict=2      ,    _A							:							List[str]=True      ,    _A							:							int="reflect"      ,    _A							:							str=2      ,    _A							:							Union[str, Any]=2      ,    _A							:							Dict=1.0      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							List[Any]=None      ,    _A							:							Any=True      ,    **_A							:							Union[str, Any]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    target_bandwidths
											_SCREAMING_SNAKE_CASE		:						Dict			  =    sampling_rate
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    audio_channels
											_SCREAMING_SNAKE_CASE		:						Any			  =    normalize
											_SCREAMING_SNAKE_CASE		:						int			  =    chunk_length_s
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    overlap
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    num_filters
											_SCREAMING_SNAKE_CASE		:						str			  =    num_residual_layers
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    upsampling_ratios
											_SCREAMING_SNAKE_CASE		:						int			  =    norm_type
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    kernel_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    last_kernel_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    residual_kernel_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    dilation_growth_rate
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    use_causal_conv
											_SCREAMING_SNAKE_CASE		:						int			  =    pad_mode
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    compress
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    num_lstm_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    trim_right_ratio
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    codebook_size
											_SCREAMING_SNAKE_CASE		:						str			  =    codebook_dim if codebook_dim is not None else hidden_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    use_conv_shortcut

											if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""")

											super().__init__(**_A)





				@property
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											if self.chunk_length_s is None:
																		return None
											else:
																		return int(self.chunk_length_s * self.sampling_rate)





				@property
				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											if self.chunk_length_s is None or self.overlap is None:
																		return None
											else:
																		return max(1      ,    int((1.0 - self.overlap) * self.chunk_length))





				@property
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    np.prod(self.upsampling_ratios)
											return math.ceil(self.sampling_rate / hop_length)





				@property
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0))



"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"   ,       "False" ) ) is not True   ,       reason="Skipping test because should only be run when releasing minor transformers version"   ,       )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											if self.framework == "pytorch":
																		subprocess.run(
																		    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()      ,    encoding="""utf-8"""      ,    check=_A      ,    )
											assert hasattr(self      ,    """env""")





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											return HuggingFace(
											    entry_point=self.script      ,    source_dir=self.env.test_path      ,    role=self.env.role      ,    image_uri=self.env.image_uri      ,    base_job_name=f"""{self.env.base_job_name}-single"""      ,    instance_count=_A      ,    instance_type=self.instance_type      ,    debugger_hook_config=_A      ,    hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,    metric_definitions=self.env.metric_definitions      ,    py_version="""py36"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											_SCREAMING_SNAKE_CASE		:						Any			  =    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

											# extract kpis
											_SCREAMING_SNAKE_CASE		:						Any			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""])
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""])
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											_SCREAMING_SNAKE_CASE		:						int			  =    (
											    Session().describe_training_job(estimator.latest_training_job.name).get("""TrainingTimeInSeconds"""      ,    9_9_9_9_9_9)
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy)
											assert all(t <= self.results["""eval_loss"""] for t in eval_loss)

											# dump tests result into json file to share in PR
											with open(f"""{estimator.latest_training_job.name}.json"""      ,    """w""") as outfile:
																		json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}      ,    _A)


