
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					lowerCamelCase		(  __lowercase  ):
     UpperCAmelCase__    :							UNetaDModel
     UpperCAmelCase__    :							KarrasVeScheduler





     def __init__(self				:   Tuple    ,   _A				:   UNetaDModel    ,   _A				:   KarrasVeScheduler       )						->						Union[str, Any]:
         super().__init__()
         self.register_modules(unet=_A    ,   scheduler=_A       )





     @torch.no_grad()
     def __call__(self				:   List[Any]    ,   _A				:   int = 1    ,   _A				:   int = 5_0    ,   _A				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _A				:   Optional[str] = "pil"    ,   _A				:   bool = True    ,   **_A				:   Dict    ,   )						->						Union[Tuple, ImagePipelineOutput]:
         snake_case								= self.unet.config.sample_size
         snake_case								= (batch_size, 3, img_size, img_size)

         snake_case								= self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         snake_case								= randn_tensor(_A    ,   generator=_A    ,   device=self.device       ) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_A       )

         for t in self.progress_bar(self.scheduler.timesteps       ):
             # here sigma_t == t_i from the paper
             snake_case								= self.scheduler.schedule[t]
             snake_case								= self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             snake_case								= self.scheduler.add_noise_to_input(_A    ,   _A    ,   generator=_A       )

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             snake_case								= (sigma_hat / 2) * model((sample_hat + 1) / 2    ,   sigma_hat / 2       ).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             snake_case								= self.scheduler.step(_A    ,   _A    ,   _A    ,   _A       )

             if sigma_prev != 0:
                 # 6. Apply 2nd order correction
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 snake_case								= (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,   sigma_prev / 2       ).sample
                 snake_case								= self.scheduler.step_correct(
                     _A    ,   _A    ,   _A    ,   _A    ,   step_output.prev_sample    ,   step_output["derivative"]    ,   )
             snake_case								= step_output.prev_sample

         snake_case								= (sample / 2 + 0.5).clamp(0    ,   1       )
         snake_case								= sample.cpu().permute(0    ,   2    ,   3    ,   1       ).numpy()
         if output_type == "pil":
             snake_case								= self.numpy_to_pil(_A       )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_A       )



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





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

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [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				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

