




"""simple docstring"""




import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:    Optional[Any]  							=							get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
     from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowerCAmelCase							:    Optional[Any]  							=							5_0003
lowerCAmelCase							:    List[str]  							=							5_0002






@require_sentencepiece
@require_tokenizers
class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






    '''simple docstring'''
    __UpperCamelCase    						=		PLBartTokenizer
    __UpperCamelCase    						=		None
    __UpperCamelCase    						=		False





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCamelCase       =      PLBartTokenizer(UpperCamelCase__	,				language_codes="""base"""	,				keep_accents=UpperCamelCase__				)
       tokenizer.save_pretrained(self.tmpdirname				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      PLBartTokenizer(UpperCamelCase__	,				language_codes="""base"""	,				keep_accents=UpperCamelCase__				)

       lowerCamelCase       =      tokenizer.tokenize("""This is a test"""				)
       self.assertListEqual(UpperCamelCase__	,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCamelCase__				)	,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,				)

       lowerCamelCase       =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]	,				)
       lowerCamelCase       =      tokenizer.convert_tokens_to_ids(UpperCamelCase__				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]	,				)

       lowerCamelCase       =      tokenizer.convert_ids_to_tokens(UpperCamelCase__				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]	,				)

       lowerCamelCase       =      tokenizer.vocab_size
       lowerCamelCase       =      [tokenizer.convert_ids_to_tokens(UpperCamelCase__				) for x in range(end - 4	,				UpperCamelCase__				)]

       self.assertListEqual(UpperCamelCase__	,				["""__java__""", """__python__""", """__en_XX__""", """<mask>"""]				)

       lowerCamelCase       =      """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
       lowerCamelCase       =      tokenizer(UpperCamelCase__				).input_ids
       self.assertEqual(
           tokenizer.decode(UpperCamelCase__	,				skip_special_tokens=UpperCamelCase__	,				clean_up_tokenization_spaces=UpperCamelCase__				)	,				UpperCamelCase__	,				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      PLBartTokenizer(UpperCamelCase__	,				language_codes="""multi"""	,				keep_accents=UpperCamelCase__				)

       lowerCamelCase       =      tokenizer.tokenize("""This is a test"""				)
       self.assertListEqual(UpperCamelCase__	,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCamelCase__				)	,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,				)

       lowerCamelCase       =      tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """9""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """é""",
               """.""",
           ]	,				)
       lowerCamelCase       =      tokenizer.convert_tokens_to_ids(UpperCamelCase__				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]	,				)

       lowerCamelCase       =      tokenizer.convert_ids_to_tokens(UpperCamelCase__				)
       self.assertListEqual(
           UpperCamelCase__	,				[
               SPIECE_UNDERLINE + """I""",
               SPIECE_UNDERLINE + """was""",
               SPIECE_UNDERLINE + """b""",
               """or""",
               """n""",
               SPIECE_UNDERLINE + """in""",
               SPIECE_UNDERLINE + """""",
               """<unk>""",
               """2""",
               """0""",
               """0""",
               """0""",
               """,""",
               SPIECE_UNDERLINE + """and""",
               SPIECE_UNDERLINE + """this""",
               SPIECE_UNDERLINE + """is""",
               SPIECE_UNDERLINE + """f""",
               """al""",
               """s""",
               """<unk>""",
               """.""",
           ]	,				)
       lowerCamelCase       =      tokenizer.vocab_size
       lowerCamelCase       =      [tokenizer.convert_ids_to_tokens(UpperCamelCase__				) for x in range(end - 7	,				UpperCamelCase__				)]

       self.assertListEqual(
           UpperCamelCase__	,				["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""]				)
       lowerCamelCase       =      """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
       lowerCamelCase       =      tokenizer(UpperCamelCase__				).input_ids
       self.assertEqual(
           tokenizer.decode(UpperCamelCase__	,				skip_special_tokens=UpperCamelCase__	,				clean_up_tokenization_spaces=UpperCamelCase__				)	,				UpperCamelCase__	,				)






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






    '''simple docstring'''
    __UpperCamelCase    						=		"uclanlp/plbart-python-en_XX"
    __UpperCamelCase    						=		[
        "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
        "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
    ]
    __UpperCamelCase    						=		[
        "Returns the maximum value of a b c.",
        "Sums the values of a b c.",
    ]
    __UpperCamelCase    						=		[
        1_34,
        54_52,
        3_34_60,
        3_34_41,
        3_34_63,
        3_34_65,
        3_34_63,
        3_34_49,
        9_88,
        20,
        3_34_56,
        19,
        3_34_56,
        7_71,
        39,
        42_58,
        8_89,
        33_18,
        3_34_41,
        3_34_63,
        3_34_65,
        3_34_63,
        3_34_49,
        24_71,
        2,
        PYTHON_CODE,
    ]





    @classmethod
    def      _lowerCAmelCase       ( cls				):





       """simple docstring"""
       lowerCamelCase       =      PLBartTokenizer.from_pretrained(
           cls.checkpoint_name	,				language_codes="""base"""	,				src_lang="""python"""	,				tgt_lang="""en_XX"""				)
       lowerCamelCase       =      1
       return cls





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""]	,				50_001				)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""]	,				50_002				)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""]	,				50_003				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
       self.assertListEqual(self.expected_src_tokens	,				UpperCamelCase__				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       self.assertIn(UpperCamelCase__	,				self.tokenizer.all_special_ids				)
       lowerCamelCase       =      [EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
       lowerCamelCase       =      self.tokenizer.decode(UpperCamelCase__	,				skip_special_tokens=UpperCamelCase__				)
       lowerCamelCase       =      self.tokenizer.decode(generated_ids[1:]	,				skip_special_tokens=UpperCamelCase__				)
       self.assertEqual(UpperCamelCase__	,				UpperCamelCase__				)
       self.assertNotIn(self.tokenizer.eos_token	,				UpperCamelCase__				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      ["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
       self.assertIsInstance(src_text[0]	,				UpperCamelCase__				)
       lowerCamelCase       =      10
       lowerCamelCase       =      self.tokenizer(UpperCamelCase__	,				max_length=UpperCamelCase__	,				truncation=UpperCamelCase__				).input_ids[0]
       self.assertEqual(ids[-2]	,				2				)
       self.assertEqual(ids[-1]	,				UpperCamelCase__				)
       self.assertEqual(len(UpperCamelCase__				)	,				UpperCamelCase__				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""]				)	,				[50_004, 50_001]				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      tempfile.mkdtemp()
       lowerCamelCase       =      self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(UpperCamelCase__				)
       lowerCamelCase       =      PLBartTokenizer.from_pretrained(UpperCamelCase__				)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,				UpperCamelCase__				)





    @require_torch
    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer(self.src_text	,				text_target=self.tgt_text	,				padding=UpperCamelCase__	,				return_tensors="""pt"""				)
       lowerCamelCase       =      shift_tokens_right(batch["""labels"""]	,				self.tokenizer.pad_token_id				)

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       self.assertEqual(batch.input_ids[1][-2:].tolist()	,				[2, PYTHON_CODE]				)
       self.assertEqual(batch.decoder_input_ids[1][0]	,				UpperCamelCase__				)
       self.assertEqual(batch.decoder_input_ids[1][-1]	,				2				)
       self.assertEqual(batch.labels[1][-2:].tolist()	,				[2, EN_CODE]				)





    @require_torch
    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer(
           self.src_text	,				text_target=self.tgt_text	,				padding=UpperCamelCase__	,				truncation=UpperCamelCase__	,				max_length=len(self.expected_src_tokens				)	,				return_tensors="""pt"""	,				)
       lowerCamelCase       =      shift_tokens_right(batch["""labels"""]	,				self.tokenizer.pad_token_id				)

       self.assertIsInstance(UpperCamelCase__	,				UpperCamelCase__				)

       self.assertEqual((2, 26)	,				batch.input_ids.shape				)
       self.assertEqual((2, 26)	,				batch.attention_mask.shape				)
       lowerCamelCase       =      batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens	,				UpperCamelCase__				)
       self.assertEqual(2	,				batch.decoder_input_ids[0, -1]				)  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens	,				[]				)
       self.assertEqual(self.tokenizer.suffix_tokens	,				[self.tokenizer.eos_token_id, PYTHON_CODE]				)





    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer(self.src_text	,				padding=UpperCamelCase__	,				truncation=UpperCamelCase__	,				max_length=3	,				return_tensors="""pt"""				)
       lowerCamelCase       =      self.tokenizer(
           text_target=self.tgt_text	,				padding=UpperCamelCase__	,				truncation=UpperCamelCase__	,				max_length=10	,				return_tensors="""pt"""				)
       lowerCamelCase       =      targets["""input_ids"""]
       lowerCamelCase       =      shift_tokens_right(UpperCamelCase__	,				self.tokenizer.pad_token_id				)

       self.assertEqual(batch.input_ids.shape[1]	,				3				)
       self.assertEqual(batch.decoder_input_ids.shape[1]	,				10				)





    @require_torch
    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer._build_translation_inputs(
           """A test"""	,				return_tensors="""pt"""	,				src_lang="""en_XX"""	,				tgt_lang="""java"""				)

       self.assertEqual(
           nested_simplify(UpperCamelCase__				)	,				{
               # A, test, EOS, en_XX
               """input_ids""": [[150, 242, 2, 50_003]],
               """attention_mask""": [[1, 1, 1, 1]],
               # java
               """forced_bos_token_id""": 50_001,
           }	,				)









"""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)



