






'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)  # pylint: disable=invalid-name







def    lowercase__			(				__UpperCamelCase   )->			Union[str, Any]:



						if not path:
												return "pipe"

						for ext in PipelineDataFormat.SUPPORTED_FORMATS:
												if path.endswith(A__   ):
																		return ext

						raise Exception(
						    F"Unable to determine file format from file extension {path}. "
						    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"   )







def    lowercase__			(				__UpperCamelCase   )->			List[str]:
						UpperCamelCase      = pipeline(
						    task=args.task		,						model=args.model if args.model else None		,						config=args.config		,						tokenizer=args.tokenizer		,						device=args.device		,						)
						UpperCamelCase      = try_infer_format_from_ext(args.input   ) if args.format == """infer""" else args.format
						UpperCamelCase      = PipelineDataFormat.from_str(
						    format=A__		,						output_path=args.output		,						input_path=args.input		,						column=args.column if args.column else nlp.default_input_names		,						overwrite=args.overwrite		,						)
						return RunCommand(A__		,						A__   )






class     a_						(      lowerCamelCase__    ):

		def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



								"""simple docstring"""






								UpperCamelCase      = nlp
								UpperCamelCase      = reader

		@staticmethod
		def   A__						(				_SCREAMING_SNAKE_CASE				)  ->				Dict:



								"""simple docstring"""






								UpperCamelCase      = parser.add_parser("""run"""      ,		help="""Run a pipeline through the CLI"""				)
								run_parser.add_argument("""--task"""      ,		choices=get_supported_tasks()      ,		help="""Task to run"""				)
								run_parser.add_argument("""--input"""      ,		type=lowercase__      ,		help="""Path to the file to use for inference"""				)
								run_parser.add_argument("""--output"""      ,		type=lowercase__      ,		help="""Path to the file that will be used post to write results."""				)
								run_parser.add_argument("""--model"""      ,		type=lowercase__      ,		help="""Name or path to the model to instantiate."""				)
								run_parser.add_argument("""--config"""      ,		type=lowercase__      ,		help="""Name or path to the model\'s config to instantiate."""				)
								run_parser.add_argument(
								    """--tokenizer"""      ,		type=lowercase__      ,		help="""Name of the tokenizer to use. (default: same as the model name)"""				)
								run_parser.add_argument(
								    """--column"""      ,		type=lowercase__      ,		help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,		)
								run_parser.add_argument(
								    """--format"""      ,		type=lowercase__      ,		default="""infer"""      ,		choices=PipelineDataFormat.SUPPORTED_FORMATS      ,		help="""Input format to read from"""      ,		)
								run_parser.add_argument(
								    """--device"""      ,		type=lowercase__      ,		default=-1      ,		help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,		)
								run_parser.add_argument("""--overwrite"""      ,		action="""store_true"""      ,		help="""Allow overwriting the output file."""				)
								run_parser.set_defaults(func=lowercase__				)


		def   A__						(				self				)  ->				Any:



								"""simple docstring"""






								UpperCamelCase  ,UpperCamelCase      = self._nlp, []

								for entry in self._reader:
														UpperCamelCase      = nlp(**lowercase__				) if self._reader.is_multi_columns else nlp(lowercase__				)
														if isinstance(lowercase__      ,		lowercase__				):
																				outputs.append(lowercase__				)
														else:
																				outputs += output

																				   # Saving data
								if self._nlp.binary_output:
														UpperCamelCase      = self._reader.save_binary(lowercase__				)
														logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}"				)
								else:
														self._reader.save(lowercase__				)








'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger('transformers.models.speecht5')







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      hf_model.apply_weight_norm()

      UpperCamelCase      = checkpoint["""input_conv.weight_g"""]
      UpperCamelCase      = checkpoint["""input_conv.weight_v"""]
      UpperCamelCase      = checkpoint["""input_conv.bias"""]

      for i in range(len(config.upsample_rates   )   ):
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_g"]
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.weight_v"]
            UpperCamelCase      = checkpoint[F"upsamples.{i}.1.bias"]

      for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
            for j in range(len(config.resblock_dilation_sizes   )   ):
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs1.{j}.1.bias"]

                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
                  UpperCamelCase      = checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

      UpperCamelCase      = checkpoint["""output_conv.1.weight_g"""]
      UpperCamelCase      = checkpoint["""output_conv.1.weight_v"""]
      UpperCamelCase      = checkpoint["""output_conv.1.bias"""]

      hf_model.remove_weight_norm()







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			List[Any]:
      if config_path is not None:
            UpperCamelCase      = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = SpeechTaHifiGanConfig()

      UpperCamelCase      = SpeechTaHifiGan(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase   )
      load_weights(orig_checkpoint["""model"""]["""generator"""]		,						__UpperCamelCase		,						__UpperCamelCase   )

      UpperCamelCase      = np.load(__UpperCamelCase   )
      UpperCamelCase      = stats[0].reshape(-1   )
      UpperCamelCase      = stats[1].reshape(-1   )
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()
      UpperCamelCase      = torch.from_numpy(__UpperCamelCase   ).float()

      model.save_pretrained(__UpperCamelCase   )

      if repo_id:
            print("""Pushing to the hub..."""   )
            model.push_to_hub(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
     parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_hifigan_checkpoint(
         args.checkpoint_path,
         args.stats_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )
