

"""simple docstring"""


import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()

			parser.add_argument(
			    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
			)
			# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
			parser.add_argument(
			    "--original_config_file",
			    default=None,
			    type=str,
			    help="The YAML config file corresponding to the original architecture.",
			)
			parser.add_argument(
			    "--num_in_channels",
			    default=None,
			    type=int,
			    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
			)
			parser.add_argument(
			    "--scheduler_type",
			    default="pndm",
			    type=str,
			    help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
			)
			parser.add_argument(
			    "--pipeline_type",
			    default=None,
			    type=str,
			    help=(
			        "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
			        ". If `None` pipeline will be automatically inferred."
			    ),
			)
			parser.add_argument(
			    "--image_size",
			    default=None,
			    type=int,
			    help=(
			        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
			        " Base. Use 768 for Stable Diffusion v2."
			    ),
			)
			parser.add_argument(
			    "--prediction_type",
			    default=None,
			    type=str,
			    help=(
			        "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
			        " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
			    ),
			)
			parser.add_argument(
			    "--extract_ema",
			    action="store_true",
			    help=(
			        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
			        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
			        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
			    ),
			)
			parser.add_argument(
			    "--upcast_attention",
			    action="store_true",
			    help=(
			        "Whether the attention computation should always be upcasted. This is necessary when running stable"
			        " diffusion 2.1."
			    ),
			)
			parser.add_argument(
			    "--from_safetensors",
			    action="store_true",
			    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
			)
			parser.add_argument(
			    "--to_safetensors",
			    action="store_true",
			    help="Whether to store pipeline in safetensors format or not.",
			)
			parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
			parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
			parser.add_argument(
			    "--stable_unclip",
			    type=str,
			    default=None,
			    required=False,
			    help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
			)
			parser.add_argument(
			    "--stable_unclip_prior",
			    type=str,
			    default=None,
			    required=False,
			    help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
			)
			parser.add_argument(
			    "--clip_stats_path",
			    type=str,
			    help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
			    required=False,
			)
			parser.add_argument(
			    "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
			)
			parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
			parser.add_argument(
			    "--vae_path",
			    type=str,
			    default=None,
			    required=False,
			    help="Set to a path, hub id to an already converted vae to not convert it again.",
			)
			__A						       =      parser.parse_args()

			__A						       =      download_from_original_stable_diffusion_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    prediction_type=args.prediction_type,
			    model_type=args.pipeline_type,
			    extract_ema=args.extract_ema,
			    scheduler_type=args.scheduler_type,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    stable_unclip=args.stable_unclip,
			    stable_unclip_prior=args.stable_unclip_prior,
			    clip_stats_path=args.clip_stats_path,
			    controlnet=args.controlnet,
			    vae_path=args.vae_path,
			)

			if args.half:
						pipe.to(torch_dtype=torch.floataa)

			if args.controlnet:
						# only save the controlnet model
						pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
			else:
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









"""simple docstring"""


import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	int   				=      args.log_outputs
		__lowerCAmelCase:	Tuple   				=      "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

		# load metric
		__lowerCAmelCase:	str   				=      load_metric("wer"			)
		__lowerCAmelCase:	int   				=      load_metric("cer"			)

		# compute metrics
		__lowerCAmelCase:	Optional[Any]   				=      wer.compute(references=result["target"] ,			predictions=result["prediction"]			)
		__lowerCAmelCase:	Dict   				=      cer.compute(references=result["target"] ,			predictions=result["prediction"]			)

		# print & log results
		__lowerCAmelCase:	Optional[int]   				=      F"WER: {wer_result}\nCER: {cer_result}"
		print(__SCREAMING_SNAKE_CASE			)

		with open(F"{dataset_id}_eval_results.txt" ,			"w"			) as f:
				f.write(__SCREAMING_SNAKE_CASE			)

		# log all results in text file. Possibly interesting for analysis
		if log_outputs is not None:
				__lowerCAmelCase:	int   				=      F"log_{dataset_id}_predictions.txt"
				__lowerCAmelCase:	Tuple   				=      F"log_{dataset_id}_targets.txt"

				with open(__SCREAMING_SNAKE_CASE ,			"w"			) as p, open(__SCREAMING_SNAKE_CASE ,			"w"			) as t:
						# mapping function to write output
						def write_to_file(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			):
								p.write(F"{i}" + "\n"			)
								p.write(batch["prediction"] + "\n"			)
								t.write(F"{i}" + "\n"			)
								t.write(batch["target"] + "\n"			)

						result.map(__SCREAMING_SNAKE_CASE ,			with_indices=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Any   				=      "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		__lowerCAmelCase:	List[Any]   				=      re.sub(__SCREAMING_SNAKE_CASE ,			"" ,			text.lower()			)

		# In addition, we can normalize the target text, e.g. removing new lines characters etc...
		# note that order is important here!
		__lowerCAmelCase:	Optional[int]   				=      ["\n\n", "\n", "   ", "  "]

		for t in token_sequences_to_ignore:
				__lowerCAmelCase:	str   				=      " ".join(text.split(__SCREAMING_SNAKE_CASE			)			)

		return text






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		# load dataset
		__lowerCAmelCase:	List[str]   				=      load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=__SCREAMING_SNAKE_CASE			)

		# for testing: only process the first two examples as a test
		# dataset = dataset.select(range(10))

		# load processor
		__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(args.model_id			)
		__lowerCAmelCase:	str   				=      feature_extractor.sampling_rate

		# resample audio
		__lowerCAmelCase:	Tuple   				=      dataset.cast_column("audio" ,			Audio(sampling_rate=__SCREAMING_SNAKE_CASE			)			)

		# load eval pipeline
		if args.device is None:
				__lowerCAmelCase:	List[Any]   				=      0 if torch.cuda.is_available() else -1
		__lowerCAmelCase:	Optional[int]   				=      pipeline("automatic-speech-recognition" ,			model=args.model_id ,			device=args.device			)

		# map function to decode audio
		def map_to_pred(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Union[str, Any]   				=      asr(
				    batch["audio"]["array"] ,			chunk_length_s=args.chunk_length_s ,			stride_length_s=args.stride_length_s			)

				__lowerCAmelCase:	Any   				=      prediction["text"]
				__lowerCAmelCase:	List[str]   				=      normalize_text(batch["sentence"]			)
				return batch

		# run inference on all examples
		__lowerCAmelCase:	str   				=      dataset.map(__SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names			)

		# compute and log_results
		# do not change function below
		log_results(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()

			parser.add_argument(
			    "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
			)
			parser.add_argument(
			    "--dataset",
			    type=str,
			    required=True,
			    help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
			)
			parser.add_argument(
			    "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
			)
			parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
			parser.add_argument(
			    "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
			)
			parser.add_argument(
			    "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
			)
			parser.add_argument(
			    "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
			)
			parser.add_argument(
			    "--device",
			    type=int,
			    default=None,
			    help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
			)
			__A						       =      parser.parse_args()

			main(args)


