






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


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

UpperCAmelCase_ :						Optional[Any]      =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_ :						Tuple      =      {
    '''vocab_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
        ),
        '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
        ),
        '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-multilingual-cased''': (
            '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-cased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_ :						Optional[int]      =      {
    '''bert-base-uncased''': 5_12,
    '''bert-large-uncased''': 5_12,
    '''bert-base-cased''': 5_12,
    '''bert-large-cased''': 5_12,
    '''bert-base-multilingual-uncased''': 5_12,
    '''bert-base-multilingual-cased''': 5_12,
    '''bert-base-chinese''': 5_12,
    '''bert-base-german-cased''': 5_12,
    '''bert-large-uncased-whole-word-masking''': 5_12,
    '''bert-large-cased-whole-word-masking''': 5_12,
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': 5_12,
    '''bert-large-cased-whole-word-masking-finetuned-squad''': 5_12,
    '''bert-base-cased-finetuned-mrpc''': 5_12,
    '''bert-base-german-dbmdz-cased''': 5_12,
    '''bert-base-german-dbmdz-uncased''': 5_12,
    '''TurkuNLP/bert-base-finnish-cased-v1''': 5_12,
    '''TurkuNLP/bert-base-finnish-uncased-v1''': 5_12,
    '''wietsedv/bert-base-dutch-cased''': 5_12,
}

UpperCAmelCase_ :						List[str]      =      {
    '''bert-base-uncased''': {'''do_lower_case''': True},
    '''bert-large-uncased''': {'''do_lower_case''': True},
    '''bert-base-cased''': {'''do_lower_case''': False},
    '''bert-large-cased''': {'''do_lower_case''': False},
    '''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
    '''bert-base-multilingual-cased''': {'''do_lower_case''': False},
    '''bert-base-chinese''': {'''do_lower_case''': False},
    '''bert-base-german-cased''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
    '''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
    '''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
    '''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
    '''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Tuple     	=  VOCAB_FILES_NAMES
	snake_case__							:     List[str]     	=  PRETRAINED_VOCAB_FILES_MAP
	snake_case__							:     Optional[Any]     	=  PRETRAINED_INIT_CONFIGURATION
	snake_case__							:     List[Any]     	=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__							:     List[str]     	=  BertTokenizer





	def __init__(		self				:					Optional[Any] ,  __lowerCamelCase				:					str=None ,  __lowerCamelCase				:					Tuple=None ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple="[UNK]" ,  __lowerCamelCase				:					Dict="[SEP]" ,  __lowerCamelCase				:					Any="[PAD]" ,  __lowerCamelCase				:					str="[CLS]" ,  __lowerCamelCase				:					Dict="[MASK]" ,  __lowerCamelCase				:					Optional[int]=True ,  __lowerCamelCase				:					Tuple=None ,  **__lowerCamelCase				:					Union[str, Any] ,  ):
		super().__init__(
		    __lowerCamelCase ,  tokenizer_file=__lowerCamelCase ,  do_lower_case=__lowerCamelCase ,  unk_token=__lowerCamelCase ,  sep_token=__lowerCamelCase ,  pad_token=__lowerCamelCase ,  cls_token=__lowerCamelCase ,  mask_token=__lowerCamelCase ,  tokenize_chinese_chars=__lowerCamelCase ,  strip_accents=__lowerCamelCase ,  **__lowerCamelCase ,  )

		UpperCamelCase     :Any          =	json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
		if (
		    normalizer_state.get("""lowercase""" ,  __lowerCamelCase	) != do_lower_case
		    or normalizer_state.get("""strip_accents""" ,  __lowerCamelCase	) != strip_accents
		    or normalizer_state.get("""handle_chinese_chars""" ,  __lowerCamelCase	) != tokenize_chinese_chars
		):
			UpperCamelCase     :Union[str, Any]          =	getattr(__lowerCamelCase ,  normalizer_state.pop("""type"""	)	)
			UpperCamelCase     :Optional[int]          =	do_lower_case
			UpperCamelCase     :Tuple          =	strip_accents
			UpperCamelCase     :str          =	tokenize_chinese_chars
			UpperCamelCase     :str          =	normalizer_class(**__lowerCamelCase	)

		UpperCamelCase     :Optional[int]          =	do_lower_case





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=None	):
		UpperCamelCase     :Tuple          =	[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 						_A (		self				:					Union[str, Any] ,  __lowerCamelCase				:					List[int] ,  __lowerCamelCase				:					Optional[List[int]] = None	):
		UpperCamelCase     :Optional[int]          =	[self.sep_token_id]
		UpperCamelCase     :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	) * [0] + len(token_ids_a + sep	) * [1]





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[str] = None	):
		UpperCamelCase     :Dict          =	self._tokenizer.model.save(__lowerCamelCase ,  name=__lowerCamelCase	)
		return tuple(__lowerCamelCase	)






from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



