





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

				UpperCAmelCase						:   str			  =     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 unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      lowerCamelCase__   (     A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							None
					__a	       =							BloomTokenizerFast
					__a	       =							BloomTokenizerFast
					__a	       =							True
					__a	       =							False
					__a	       =							"""tokenizer_file"""
					__a	       =							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												super().setUp()
												__UpperCAmelCase   :						Any      							=					BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""  )
												tokenizer.save_pretrained(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Any ,       **UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												kwargs.update(self.special_tokens_map  )
												return BloomTokenizerFast.from_pretrained(self.tmpdirname ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.get_rust_tokenizer()

												__UpperCAmelCase   :						Optional[Any]      							=					["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
												__UpperCAmelCase   :						int      							=					[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

												__UpperCAmelCase   :						Dict      							=					tokenizer.batch_encode_plus(UpperCamelCase  )["""input_ids"""]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					tokenizer.batch_decode(UpperCamelCase  )
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Any=6  ):
												'''simple docstring'''






												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																										__UpperCAmelCase   :						Optional[int]      							=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase ,       **UpperCamelCase  )
																										# tokenizer_r.pad_token = None # Hotfixing padding = None
																										# Simple input
																										__UpperCAmelCase   :						Dict      							=					"""This is a simple input"""
																										__UpperCAmelCase   :						str      							=					["""This is a simple input 1""", """This is a simple input 2"""]
																										__UpperCAmelCase   :						List[str]      							=					("""This is a simple input""", """This is a pair""")
																										__UpperCAmelCase   :						Dict      							=					[
																										    ("""This is a simple input 1""", """This is a simple input 2"""),
																										    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																										]

																										# Simple input tests
																										try:
																																	tokenizer_r.encode(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )

																																	tokenizer_r.batch_encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.encode(UpperCamelCase ,       max_length=UpperCamelCase  )
																																	tokenizer_r.batch_encode_plus(UpperCamelCase ,       max_length=UpperCamelCase  )
																										except ValueError:
																																	self.fail("""Bloom Tokenizer should be able to deal with padding"""  )

																										__UpperCAmelCase   :						Union[str, Any]      							=					None  # Hotfixing padding = None
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Simple input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Simple input
																										self.assertRaises(
																										    UpperCamelCase ,       tokenizer_r.batch_encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length""" ,       )

																										# Pair input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Pair input
																										self.assertRaises(UpperCamelCase ,       tokenizer_r.encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length"""  )

																										# Pair input
																										self.assertRaises(
																										    UpperCamelCase ,       tokenizer_r.batch_encode_plus ,       UpperCamelCase ,       max_length=UpperCamelCase ,       padding="""max_length""" ,       )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					self.get_rust_tokenizer()
												__UpperCAmelCase   :						Optional[Any]      							=					load_dataset("""xnli""" ,       """all_languages""" ,       split="""test""" ,       streaming=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					next(iter(UpperCamelCase  )  )["""premise"""]  # pick up one data
												__UpperCAmelCase   :						Any      							=					list(sample_data.values()  )

												__UpperCAmelCase   :						Optional[Any]      							=					list(map(tokenizer.encode ,       UpperCamelCase  )  )
												__UpperCAmelCase   :						List[Any]      							=					[tokenizer.decode(UpperCamelCase ,       clean_up_tokenization_spaces=UpperCamelCase  ) for x in output_tokens]
												self.assertListEqual(UpperCamelCase ,       UpperCamelCase  )






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map  ) ,       1  )
												self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()  )[0]  ) ,       1  )
