




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 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{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."     )

					__lowerCAmelCase				   =       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 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
											pl_module.logger.log_metrics(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       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.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=_A			)
																generations_file.parent.mkdir(exist_ok=_A			)
											with open(_A		,			"a+"			) as writer:
																for key in sorted(_A			):
																					if key in ["log", "progress_bar", "preds"]:
																										continue
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																__lowerCAmelCase				   =       pl_module.model.model.num_parameters()
											except AttributeError:
																__lowerCAmelCase				   =       pl_module.model.num_parameters()

											__lowerCAmelCase				   =       count_trainable_parameters(_A			)
											# 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		,			_A		,			_A			):




											"""simple docstring"""



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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""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"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
       from datasets import load_dataset






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """microsoft/speecht5_tts"""
 _lowerCamelCase        =     (
     """This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
     """text to read (in English) and returns a waveform object containing the sound."""
 )
 _lowerCamelCase        =     """text_reader"""
 _lowerCamelCase        =     SpeechTaProcessor
 _lowerCamelCase        =     SpeechTaForTextToSpeech
 _lowerCamelCase        =     SpeechTaHifiGan

 _lowerCamelCase        =     ["""text"""]
 _lowerCamelCase        =     ["""audio"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     self.pre_processor(text=__lowerCamelCase					,						return_tensors='''pt'''					,						truncation=__lowerCamelCase  )

     if speaker_embeddings is None:
         if not is_datasets_available():
             raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''  )

         __A    :						List[Any]						 =     load_dataset('''Matthijs/cmu-arctic-xvectors'''					,						split='''validation'''  )
         __A    :						int						 =     torch.tensor(embeddings_dataset[7305]['''xvector''']  ).unsqueeze(0  )

     return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.model.generate_speech(**__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.post_processor(__lowerCamelCase  ).cpu().detach()




