


"""simple docstring"""







import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =DebertaTokenizer
       a  :    str       =True
       a  :    Tuple       =DebertaTokenizerFast

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCAmelCase					:					str								=							[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "[UNK]",
         ]
         lowerCAmelCase					:					Dict								=							dict(zip(snake_case__		,					range(len(snake_case__						)						)						)						)
         lowerCAmelCase					:					List[str]								=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowerCAmelCase					:					Union[str, Any]								=							{"unk_token": "[UNK]"}

         lowerCAmelCase					:					str								=							os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES["vocab_file"]						)
         lowerCAmelCase					:					Union[str, Any]								=							os.path.join(self.tmpdirname		,					VOCAB_FILES_NAMES["merges_file"]						)
         with open(self.vocab_file		,					"w"		,					encoding="utf-8"						) as fp:
           fp.write(json.dumps(snake_case__						) + "\n"						)
         with open(self.merges_file		,					"w"		,					encoding="utf-8"						) as fp:
           fp.write("\n".join(snake_case__						)						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return self.tokenizer_class.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							"lower newer"
         lowerCAmelCase					:					Dict								=							"lower newer"
         return input_text, output_text

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.get_tokenizer()
         lowerCAmelCase					:					Union[str, Any]								=							"lower newer"
         lowerCAmelCase					:					Optional[int]								=							["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
         lowerCAmelCase					:					Tuple								=							tokenizer.tokenize(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Any								=							tokens + [tokenizer.unk_token]
         lowerCAmelCase					:					Dict								=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__						)		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.get_tokenizer()
         lowerCAmelCase					:					int								=							tokenizer("Hello"		,					"World"						)
         lowerCAmelCase					:					Union[str, Any]								=							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["token_type_ids"]		,					snake_case__						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.tokenizer_class.from_pretrained("microsoft/deberta-base"						)

         lowerCAmelCase					:					Dict								=							tokenizer.encode("sequence builders"		,					add_special_tokens=snake_case__						)
         lowerCAmelCase					:					Tuple								=							tokenizer.encode("multi-sequence build"		,					add_special_tokens=snake_case__						)

         lowerCAmelCase					:					List[Any]								=							tokenizer.encode(
             "sequence builders"		,					add_special_tokens=snake_case__		,					add_prefix_space=snake_case__						)
         lowerCAmelCase					:					List[Any]								=							tokenizer.encode(
             "sequence builders"		,					"multi-sequence build"		,					add_special_tokens=snake_case__		,					add_prefix_space=snake_case__						)

         lowerCAmelCase					:					Tuple								=							tokenizer.build_inputs_with_special_tokens(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							tokenizer.build_inputs_with_special_tokens(snake_case__		,					snake_case__						)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							[self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

         for tokenizer_class in tokenizer_classes:
           lowerCAmelCase					:					Any								=							tokenizer_class.from_pretrained("microsoft/deberta-base"						)

           lowerCAmelCase					:					Optional[int]								=							[
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           lowerCAmelCase					:					List[Any]								=							tokenizer(snake_case__		,					padding=snake_case__						)
           lowerCAmelCase					:					Optional[int]								=							[tokenizer.decode(snake_case__		,					skip_special_tokens=snake_case__						) for seq in encoding["input_ids"]]

           # fmt: off
           lowerCAmelCase					:					int								=							{
               "input_ids": [
                   [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           lowerCAmelCase					:					Optional[Any]								=							[
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data		,					snake_case__						)

           for expected, decoded in zip(snake_case__		,					snake_case__						):
             self.assertEqual(snake_case__		,					snake_case__						)









"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     lowerCAmelCase__				    =       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=512,
         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  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''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)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       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)





