
import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [7, 4, 5, 10, 8, 9]							)





	def      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)
				parser.add_argument(
				    """--original_config_file""",
				    type=str,
				    required=True,
				    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(
				    """--image_size""",
				    default=5_12,
				    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(
				    """--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.)""")






				def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''simple docstring'''


									if string == "True":
														return True
									elif string == "False":
														return False
									else:
														raise ValueError(F'could not parse string as bool {string}'		)

				parser.add_argument(
				    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
				)

				parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

				__snake_case								=  parser.parse_args()

				__snake_case								=  download_controlnet_from_original_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    extract_ema=args.extract_ema,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    use_linear_projection=args.use_linear_projection,
				    cross_attention_dim=args.cross_attention_dim,
				)



				controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





