


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 						__a :
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase											=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'		)

									torch.manual_seed(0		)
									_UpperCAmelCase											=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'		)

									torch.manual_seed(0		)
									_UpperCAmelCase											=     UNetaDConditionModel(
									    sample_size=32     ,       layers_per_block=1     ,       block_out_channels=[32, 64]     ,       down_block_types=[
									        'ResnetDownsampleBlock2D',
									        'SimpleCrossAttnDownBlock2D',
									    ]     ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,       in_channels=3     ,       out_channels=6     ,       cross_attention_dim=32     ,       encoder_hid_dim=32     ,       attention_head_dim=8     ,       addition_embed_type='text'     ,       addition_embed_type_num_heads=2     ,       cross_attention_norm='group_norm'     ,       resnet_time_scale_shift='scale_shift'     ,       act_fn='gelu'     ,       )
									unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

									torch.manual_seed(0		)
									_UpperCAmelCase											=     DDPMScheduler(
									    num_train_timesteps=1000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0001     ,       beta_end=0.02     ,       thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0.95     ,       sample_max_value=1.0     ,       prediction_type='epsilon'     ,       variance_type='learned_range'     ,       )

									torch.manual_seed(0		)
									_UpperCAmelCase											=     IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									torch.manual_seed(0		)
									_UpperCAmelCase											=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'		)

									torch.manual_seed(0		)
									_UpperCAmelCase											=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'		)

									torch.manual_seed(0		)
									_UpperCAmelCase											=     UNetaDConditionModel(
									    sample_size=32     ,       layers_per_block=[1, 2]     ,       block_out_channels=[32, 64]     ,       down_block_types=[
									        'ResnetDownsampleBlock2D',
									        'SimpleCrossAttnDownBlock2D',
									    ]     ,       mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,       up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,       in_channels=6     ,       out_channels=6     ,       cross_attention_dim=32     ,       encoder_hid_dim=32     ,       attention_head_dim=8     ,       addition_embed_type='text'     ,       addition_embed_type_num_heads=2     ,       cross_attention_norm='group_norm'     ,       resnet_time_scale_shift='scale_shift'     ,       act_fn='gelu'     ,       class_embed_type='timestep'     ,       mid_block_scale_factor=1.414     ,       time_embedding_act_fn='gelu'     ,       time_embedding_dim=32     ,       )
									unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

									torch.manual_seed(0		)
									_UpperCAmelCase											=     DDPMScheduler(
									    num_train_timesteps=1000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0001     ,       beta_end=0.02     ,       thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0.95     ,       sample_max_value=1.0     ,       prediction_type='epsilon'     ,       variance_type='learned_range'     ,       )

									torch.manual_seed(0		)
									_UpperCAmelCase											=     DDPMScheduler(
									    num_train_timesteps=1000     ,       beta_schedule='squaredcos_cap_v2'     ,       beta_start=0.0001     ,       beta_end=0.02     ,       )

									torch.manual_seed(0		)
									_UpperCAmelCase											=     IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "image_noising_scheduler": image_noising_scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_dummy_components()
									_UpperCAmelCase											=     self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
									pipe.to(_SCREAMING_SNAKE_CASE		)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     inputs["prompt"]
									_UpperCAmelCase											=     inputs["generator"]
									_UpperCAmelCase											=     inputs["num_inference_steps"]
									_UpperCAmelCase											=     inputs["output_type"]

									if "image" in inputs:
																_UpperCAmelCase											=     inputs["image"]
									else:
																_UpperCAmelCase											=     None

									if "mask_image" in inputs:
																_UpperCAmelCase											=     inputs["mask_image"]
									else:
																_UpperCAmelCase											=     None

									if "original_image" in inputs:
																_UpperCAmelCase											=     inputs["original_image"]
									else:
																_UpperCAmelCase											=     None

									_UpperCAmelCase											=     pipe.encode_prompt(_SCREAMING_SNAKE_CASE		)

									# inputs with prompt converted to embeddings
									_UpperCAmelCase											=     {
									    "prompt_embeds": prompt_embeds,
									    "negative_prompt_embeds": negative_prompt_embeds,
									    "generator": generator,
									    "num_inference_steps": num_inference_steps,
									    "output_type": output_type,
									}

									if image is not None:
																_UpperCAmelCase											=     image

									if mask_image is not None:
																_UpperCAmelCase											=     mask_image

									if original_image is not None:
																_UpperCAmelCase											=     original_image

									# set all optional components to None
									for optional_component in pipe._optional_components:
																setattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     pipe(**_SCREAMING_SNAKE_CASE		)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
																pipe_loaded.to(_SCREAMING_SNAKE_CASE		)
																pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

									for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) is None     ,       f'''`{optional_component}` did not stay set to None after loading.'''     ,       )

									_UpperCAmelCase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     inputs["generator"]
									_UpperCAmelCase											=     inputs["num_inference_steps"]
									_UpperCAmelCase											=     inputs["output_type"]

									# inputs with prompt converted to embeddings
									_UpperCAmelCase											=     {
									    "prompt_embeds": prompt_embeds,
									    "negative_prompt_embeds": negative_prompt_embeds,
									    "generator": generator,
									    "num_inference_steps": num_inference_steps,
									    "output_type": output_type,
									}

									if image is not None:
																_UpperCAmelCase											=     image

									if mask_image is not None:
																_UpperCAmelCase											=     mask_image

									if original_image is not None:
																_UpperCAmelCase											=     original_image

									_UpperCAmelCase											=     pipe_loaded(**_SCREAMING_SNAKE_CASE		)[0]

									_UpperCAmelCase											=     np.abs(to_np(_SCREAMING_SNAKE_CASE		) - to_np(_SCREAMING_SNAKE_CASE		)		).max()
									self.assertLess(_SCREAMING_SNAKE_CASE     ,       1e-4		)






		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_dummy_components()
									_UpperCAmelCase											=     self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
									pipe.to(_SCREAMING_SNAKE_CASE		)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     pipe(**_SCREAMING_SNAKE_CASE		)[0]

									with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
																pipe_loaded.to(_SCREAMING_SNAKE_CASE		)
																pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

									_UpperCAmelCase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     pipe_loaded(**_SCREAMING_SNAKE_CASE		)[0]

									_UpperCAmelCase											=     np.abs(to_np(_SCREAMING_SNAKE_CASE		) - to_np(_SCREAMING_SNAKE_CASE		)		).max()
									self.assertLess(_SCREAMING_SNAKE_CASE     ,       1e-4		)



"""simple docstring"""

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    """▁"""

_UpperCamelCase		     =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

_UpperCamelCase		     =    {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

_UpperCamelCase		     =    {"""vinai/bartpho-syllable""": 1_0_2_4}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      int    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      List[str]    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      List[Any]    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      int    =						['input_ids', 'attention_mask']






       def __init__(   self			,					snake_case			,					snake_case			,					snake_case="<s>"			,					snake_case="</s>"			,					snake_case="</s>"			,					snake_case="<s>"			,					snake_case="<unk>"			,					snake_case="<pad>"			,					snake_case="<mask>"			,					snake_case = None			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				AddedToken(snake_case			,					lstrip=snake_case			,					rstrip=snake_case   ) if isinstance(snake_case			,					snake_case   ) else mask_token

        lowerCAmelCase__   :				Union[str, Any] 			=				{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=snake_case			,					eos_token=snake_case			,					unk_token=snake_case			,					sep_token=snake_case			,					cls_token=snake_case			,					pad_token=snake_case			,					mask_token=snake_case			,					sp_model_kwargs=self.sp_model_kwargs			,					**snake_case			,					)

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_file
        lowerCAmelCase__   :				Optional[Any] 			=				monolingual_vocab_file
        lowerCAmelCase__   :				Optional[Any] 			=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.Load(str(snake_case   )   )

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        lowerCAmelCase__   :				Optional[int] 			=				{}
        lowerCAmelCase__   :				int 			=				0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(snake_case   ) not in self.fairseq_tokens_to_ids:
          lowerCAmelCase__   :				Optional[int] 			=				cnt
          cnt += 1
        with open(snake_case			,					"r"			,					encoding="utf-8"   ) as f:
         for line in f.readlines():
          lowerCAmelCase__   :				Union[str, Any] 			=				line.strip().split()[0]
          lowerCAmelCase__   :				List[str] 			=				len(self.fairseq_tokens_to_ids   )
        if str(snake_case   ) not in self.fairseq_tokens_to_ids:
         lowerCAmelCase__   :				int 			=				len(self.fairseq_tokens_to_ids   )

        lowerCAmelCase__   :				Union[str, Any] 			=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.__dict__.copy()
        lowerCAmelCase__   :				str 			=				None
        lowerCAmelCase__   :				Union[str, Any] 			=				self.sp_model.serialized_model_proto()
        return state






       def __setstate__(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				d

        # for backward compatibility
        if not hasattr(self			,					"sp_model_kwargs"   ):
         lowerCAmelCase__   :				Any 			=				{}

        lowerCAmelCase__   :				Dict 			=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowerCAmelCase__   :				Any 			=				[self.cls_token_id]
        lowerCAmelCase__   :				Dict 			=				[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None			,					snake_case = False   ):





        """simple docstring"""

        if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=snake_case			,					token_ids_a=snake_case			,					already_has_special_tokens=snake_case   )

        if token_ids_a is None:
         return [1] + ([0] * len(snake_case   )) + [1]
        return [1] + ([0] * len(snake_case   )) + [1, 1] + ([0] * len(snake_case   )) + [1]






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				[self.sep_token_id]
        lowerCAmelCase__   :				Optional[int] 			=				[self.cls_token_id]

        if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return len(self.fairseq_ids_to_tokens   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
        vocab.update(self.added_tokens_encoder   )
        return vocab






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return self.sp_model.encode(snake_case			,					out_type=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        else:
         return self.unk_token_id






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        return self.fairseq_ids_to_tokens[index]






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				"".join(snake_case   ).replace(snake_case			,					" "   ).strip()
        return out_string






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
        lowerCAmelCase__   :				List[str] 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
        lowerCAmelCase__   :				Optional[Any] 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]			,					)

        if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file			,					snake_case   )
        elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case			,					"wb"   ) as fi:
          lowerCAmelCase__   :				str 			=				self.sp_model.serialized_model_proto()
          fi.write(snake_case   )

        if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
            snake_case   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file			,					snake_case   )
        elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(snake_case			,					"w"			,					encoding="utf-8"   ) as fp:
          for token in self.fairseq_tokens_to_ids:
           if token not in self.all_special_tokens:
            fp.write(F"""{str(snake_case   )} \n"""   )

        return out_vocab_file, out_monolingual_vocab_file






