



"""simple docstring"""






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case       (	unittest.TestCase):




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				Optional[int]    			= UNetaDModel(
             block_out_channels=(3_2, 6_4)						,					layers_per_block=2						,					sample_size=3_2						,					in_channels=3						,					out_channels=3						,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')						,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')						,					)
         return model




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				int    			= VQModel(
             block_out_channels=[3_2, 6_4]						,					in_channels=3						,					out_channels=3						,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,					latent_channels=3						,					)
         return model




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				Union[str, Any]    			= CLIPTextConfig(
             bos_token_id=0						,					eos_token_id=2						,					hidden_size=3_2						,					intermediate_size=3_7						,					layer_norm_eps=1E-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1_0_0_0						,					)
         return CLIPTextModel(_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.dummy_uncond_unet
         _lowerCamelCase						:				Optional[Any]    			= DDIMScheduler()
         _lowerCamelCase						:				Optional[int]    			= self.dummy_vq_model

         _lowerCamelCase						:				Optional[int]    			= LDMPipeline(unet=_A						,					vqvae=_A						,					scheduler=_A		)
         ldm.to(_A		)
         ldm.set_progress_bar_config(disable=_A		)

         _lowerCamelCase						:				Dict    			= torch.manual_seed(0		)
         _lowerCamelCase						:				str    			= ldm(generator=_A						,					num_inference_steps=2						,					output_type='''numpy'''		).images

         _lowerCamelCase						:				int    			= torch.manual_seed(0		)
         _lowerCamelCase						:				List[Any]    			= ldm(generator=_A						,					num_inference_steps=2						,					output_type='''numpy'''						,					return_dict=_A		)[0]

         _lowerCamelCase						:				Any    			= image[0, -3:, -3:, -1]
         _lowerCamelCase						:				Optional[Any]    			= image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCamelCase						:				Optional[Any]    			= np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]		)
         _lowerCamelCase						:				int    			= 1E-2 if torch_device != "mps" else 3E-2

         assert np.abs(image_slice.flatten() - expected_slice		).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < tolerance






@slow
@require_torch
class        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''		)
         ldm.to(_A		)
         ldm.set_progress_bar_config(disable=_A		)

         _lowerCamelCase						:				Union[str, Any]    			= torch.manual_seed(0		)
         _lowerCamelCase						:				List[Any]    			= ldm(generator=_A						,					num_inference_steps=5						,					output_type='''numpy'''		).images

         _lowerCamelCase						:				int    			= image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         _lowerCamelCase						:				Optional[int]    			= np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]		)
         _lowerCamelCase						:				List[Any]    			= 1E-2 if torch_device != "mps" else 3E-2

         assert np.abs(image_slice.flatten() - expected_slice		).max() < tolerance



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__lowerCamelCase					:	Optional[Any]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Dict       =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase					:	int       =  {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__lowerCamelCase					:	Any       =  {
    '''junnyu/roformer_chinese_small''': 15_36,
    '''junnyu/roformer_chinese_base''': 15_36,
    '''junnyu/roformer_chinese_char_small''': 5_12,
    '''junnyu/roformer_chinese_char_base''': 5_12,
    '''junnyu/roformer_small_discriminator''': 1_28,
    '''junnyu/roformer_small_generator''': 1_28,
}


__lowerCamelCase					:	Union[str, Any]       =  {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}






class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							PRETRAINED_INIT_CONFIGURATION
					A	       =							RoFormerTokenizer

					def __init__(    self      :       List[str],_A      :       int=None,_A      :       int=None,_A      :       int=True,_A      :       List[Any]="[UNK]",_A      :       Tuple="[SEP]",_A      :       List[Any]="[PAD]",_A      :       Optional[int]="[CLS]",_A      :       Optional[Any]="[MASK]",_A      :       Optional[int]=True,_A      :       List[str]=None,**_A      :       List[Any],):







							"""simple docstring"""






							super().__init__(
							    _A,tokenizer_file=_A,do_lower_case=_A,unk_token=_A,sep_token=_A,pad_token=_A,cls_token=_A,mask_token=_A,tokenize_chinese_chars=_A,strip_accents=_A,**_A,)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
							if (
							    pre_tok_state.get("lowercase",_A    ) != do_lower_case
							    or pre_tok_state.get("strip_accents",_A    ) != strip_accents
							):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    getattr(_A,pre_tok_state.pop("type"    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    do_lower_case
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    strip_accents
									SCREAMING_SNAKE_CASE_  :		str				      =    pre_tok_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_lower_case

					def __getstate__(    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    BertPreTokenizer()
							return state

					def __setstate__(    self      :       List[Any],_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    d
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.__dict__["_tokenizer"].get_vocab()
							SCREAMING_SNAKE_CASE_  :		Any				      =    PreTokenizer.custom(JiebaPreTokenizer(_A    )    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       List[Any],_A      :       str=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [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    ) * [0] + len(token_ids_a + sep    ) * [1]

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._tokenizer.model.save(_A,name=_A    )
							return tuple(_A    )






					def 				__UpperCamelCase   (    self      :       int,_A      :       Optional[int],_A      :       List[Any]=None,_A      :       Tuple=None,_A      :       str=False,**_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BertPreTokenizer()
							return super().save_pretrained(_A,_A,_A,_A,**_A    )
