'''simple docstring'''




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						_lowercase		:				Optional[Any]	       =		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.",
						)
						_lowercase		:				Union[str, Any]	       =		parser.parse_args()

						_lowercase		:				Tuple	       =		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'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





