



"""simple docstring"""
import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def    _snake_case		(		UpperCamelCase		:	Optional[int]		):
 UpperCAmelCase			:      List[Any]			=			filter(lambda UpperCamelCase		: p.requires_grad    ,	model.parameters()		)
 UpperCAmelCase			:      Optional[Any]			=			sum([np.prod(p.size()		) for p in model_parameters]		)
 return params


A:     List[Any]										=	logging.getLogger(__name__)

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Any		):
 if metric == "rouge2":
  UpperCAmelCase			:      str			=			"""{val_avg_rouge2:.4f}-{step_count}"""
 elif metric == "bleu":
  UpperCAmelCase			:      Tuple			=			"""{val_avg_bleu:.4f}-{step_count}"""
 elif metric == "em":
  UpperCAmelCase			:      List[str]			=			"""{val_avg_em:.4f}-{step_count}"""
 else:
  raise NotImplementedError(
      F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
      """ function."""		)

 UpperCAmelCase			:      Any			=			ModelCheckpoint(
     dirpath=_SCREAMING_SNAKE_CASE    ,	filename=_SCREAMING_SNAKE_CASE    ,	monitor=F"val_{metric}"    ,	mode="""max"""    ,	save_top_k=3    ,	every_n_epochs=1    ,	)
 return checkpoint_callback

def    _snake_case		(		UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	str		):
 return EarlyStopping(
     monitor=F"val_{metric}"    ,	mode="""min""" if """loss""" in metric else """max"""    ,	patience=_SCREAMING_SNAKE_CASE    ,	verbose=_SCREAMING_SNAKE_CASE    ,	)



class    SCREAMING_SNAKE_CASE__						(      pl.Callback				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
    pl_module.logger.log_metrics(UpperCAmelCase_		)

   @rank_zero_only
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=True		)						->   None:





    '''simple docstring'''




    logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
    UpperCAmelCase			:      List[Any]			=			trainer.callback_metrics
    trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
    # Log results
    UpperCAmelCase			:      Optional[int]			=			Path(pl_module.hparams.output_dir		)
    if type_path == "test":
     UpperCAmelCase			:      Dict			=			od / """test_results.txt"""
     UpperCAmelCase			:      int			=			od / """test_generations.txt"""
    else:
     # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
     # If people want this it will be easy enough to add back.
     UpperCAmelCase			:      Optional[Any]			=			od / F"{type_path}_results/{trainer.global_step:05d}.txt"
     UpperCAmelCase			:      List[str]			=			od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
     results_file.parent.mkdir(exist_ok=UpperCAmelCase_		)
     generations_file.parent.mkdir(exist_ok=UpperCAmelCase_		)
    with open(UpperCAmelCase_   ,  """a+"""		) as writer:
     for key in sorted(UpperCAmelCase_		):
      if key in ["log", "progress_bar", "preds"]:
       continue
      UpperCAmelCase			:      Optional[Any]			=			metrics[key]
      if isinstance(UpperCAmelCase_   ,  torch.Tensor		):
       UpperCAmelCase			:      str			=			val.item()
      UpperCAmelCase			:      Any			=			F"{key}: {val:.6f}\n"
      writer.write(UpperCAmelCase_		)

    if not save_generations:
     return

    if "preds" in metrics:
     UpperCAmelCase			:      int			=			"""\n""".join(metrics["""preds"""]		)
     generations_file.open("""w+"""		).write(UpperCAmelCase_		)

   @rank_zero_only
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




    try:
     UpperCAmelCase			:      Union[str, Any]			=			pl_module.model.model.num_parameters()
    except AttributeError:
     UpperCAmelCase			:      Any			=			pl_module.model.num_parameters()

    UpperCAmelCase			:      Optional[int]			=			count_trainable_parameters(UpperCAmelCase_		)
    # mp stands for million parameters
    trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)

   @rank_zero_only
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    save_json(pl_module.metrics   ,  pl_module.metrics_save_path		)
    return self._write_logs(UpperCAmelCase_   ,  UpperCAmelCase_   ,  """test"""		)




   @rank_zero_only
   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    save_json(pl_module.metrics   ,  pl_module.metrics_save_path		)
    # Uncommenting this will save val generations
    # return self._write_logs(trainer, pl_module, "valid")










"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def    _snake_case		(		UpperCamelCase		:	bytes    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      Optional[int]			=			"""1"""
 UpperCAmelCase			:      Dict			=			"""f32le"""
 UpperCAmelCase			:      Optional[int]			=			[
     """ffmpeg""",
     """-i""",
     """pipe:0""",
     """-ac""",
     ac,
     """-ar""",
     ar,
     """-f""",
     format_for_conversion,
     """-hide_banner""",
     """-loglevel""",
     """quiet""",
     """pipe:1""",
 ]

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	str = "f32le"    ,	):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      List[str]			=			"""1"""
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      int			=			2
 elif format_for_conversion == "f32le":
  UpperCAmelCase			:      int			=			4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"		)

 UpperCAmelCase			:      Optional[Any]			=			platform.system()
 if system == "Linux":
  UpperCAmelCase			:      Tuple			=			"""alsa"""
  UpperCAmelCase			:      Any			=			"""default"""
 elif system == "Darwin":
  UpperCAmelCase			:      Any			=			"""avfoundation"""
  UpperCAmelCase			:      int			=			""":0"""
 elif system == "Windows":
  UpperCAmelCase			:      Optional[int]			=			"""dshow"""
  UpperCAmelCase			:      str			=			"""default"""

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	Optional[int] = None    ,	UpperCamelCase		:	Optional[Union[Tuple[float, float], float]] = None    ,	UpperCamelCase		:	str = "f32le"    ,	):
 if stream_chunk_s is not None:
  UpperCAmelCase			:      List[Any]			=			stream_chunk_s
 else:
  UpperCAmelCase			:      List[Any]			=			chunk_length_s

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

 if stride_length_s is None:
  UpperCAmelCase			:      Tuple			=			chunk_length_s / 6
 UpperCAmelCase			:      Optional[Any]			=			int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
 if isinstance(UpperCamelCase    ,	(int, float)		):
  UpperCAmelCase			:      str			=			[stride_length_s, stride_length_s]

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

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Tuple[int, int]    ,	UpperCamelCase		:	bool = False		):
 UpperCAmelCase			:      Any			=			B""""""
 UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			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			:      int			=			0
 for raw in iterator:
  acc += raw
  if stream and len(UpperCamelCase		) < chunk_len:
   UpperCAmelCase			:      Optional[Any]			=			(_stride_left, 0)
   yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
  else:
   while len(UpperCamelCase		) >= chunk_len:
    # We are flushing the accumulator
    UpperCAmelCase			:      List[str]			=			(_stride_left, stride_right)
    UpperCAmelCase			:      List[str]			=			{"""raw""": acc[:chunk_len], """stride""": stride}
    if stream:
     UpperCAmelCase			:      List[str]			=			False
    yield item
    UpperCAmelCase			:      Optional[Any]			=			stride_left
    UpperCAmelCase			:      Union[str, Any]			=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
 if len(UpperCamelCase		) > stride_left:
  UpperCAmelCase			:      Union[str, Any]			=			{"""raw""": acc, """stride""": (_stride_left, 0)}
  if stream:
   UpperCAmelCase			:      str			=			False
  yield item

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      List[Any]			=			2**24  # 16Mo
 try:
  with subprocess.Popen(UpperCamelCase    ,	stdout=subprocess.PIPE    ,	bufsize=UpperCamelCase		) as ffmpeg_process:
   while True:
    UpperCAmelCase			:      Optional[Any]			=			ffmpeg_process.stdout.read(UpperCamelCase		)
    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
