




"""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 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							super().setUp()
							lowerCamelCase       =      BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
							tokenizer.save_pretrained(self.tmpdirname				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							kwargs.update(self.special_tokens_map				)
							return BloomTokenizerFast.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_rust_tokenizer()

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

							lowerCamelCase       =      tokenizer.batch_encode_plus(_a				)["""input_ids"""]
							self.assertListEqual(_a	,				_a				)

							lowerCamelCase       =      tokenizer.batch_decode(_a				)
							self.assertListEqual(_a	,				_a				)





				def      _lowerCAmelCase       ( self	,				_a=6				):





							"""simple docstring"""
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'				):
													lowerCamelCase       =      self.rust_tokenizer_class.from_pretrained(_a	,				**_a				)
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													lowerCamelCase       =      """This is a simple input"""
													lowerCamelCase       =      ["""This is a simple input 1""", """This is a simple input 2"""]
													lowerCamelCase       =      ("""This is a simple input""", """This is a pair""")
													lowerCamelCase       =      [
													    ("""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(_a	,				max_length=_a				)
																tokenizer_r.encode_plus(_a	,				max_length=_a				)

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

													lowerCamelCase       =      None  # Hotfixing padding = None
													self.assertRaises(_a	,				tokenizer_r.encode	,				_a	,				max_length=_a	,				padding="""max_length"""				)

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

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

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

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

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_rust_tokenizer()
							lowerCamelCase       =      load_dataset("""xnli"""	,				"""all_languages"""	,				split="""test"""	,				streaming=_a				)

							lowerCamelCase       =      next(iter(_a				)				)["""premise"""]  # pick up one data
							lowerCamelCase       =      list(sample_data.values()				)

							lowerCamelCase       =      list(map(tokenizer.encode	,				_a				)				)
							lowerCamelCase       =      [tokenizer.decode(_a	,				clean_up_tokenization_spaces=_a				) for x in output_tokens]
							self.assertListEqual(_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							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				)









"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							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.""",
					)
					lowerCAmelCase							:    Dict  							=							parser.parse_args()

					lowerCAmelCase							:    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)



