


'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class   lowercase      (				A__   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCamelCase__  :Tuple				   =					DDIMScheduler.from_config(scheduler.config				)

							self.register_modules(unet=UpperCamelCase_       ,  scheduler=UpperCamelCase_				)




		@torch.no_grad()
		def __call__(						self       ,  UpperCamelCase_ = 1       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = 0.0       ,  UpperCamelCase_ = 50       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = "pil"       ,  UpperCamelCase_ = True       ,  ):

							'''simple docstring'''


							if isinstance(self.unet.config.sample_size       ,  UpperCamelCase_				):
												UpperCamelCase__  :Tuple				   =					(
												    batch_size,
												    self.unet.config.in_channels,
												    self.unet.config.sample_size,
												    self.unet.config.sample_size,
												)
							else:
												UpperCamelCase__  :Dict				   =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				) and len(UpperCamelCase_				) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(UpperCamelCase_				)}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

							UpperCamelCase__  :Optional[int]				   =					randn_tensor(UpperCamelCase_       ,  generator=UpperCamelCase_       ,  device=self.device       ,  dtype=self.unet.dtype				)

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

							for t in self.progress_bar(self.scheduler.timesteps				):
												# 1. predict noise model_output
												UpperCamelCase__  :Any				   =					self.unet(UpperCamelCase_       ,  UpperCamelCase_				).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												UpperCamelCase__  :Any				   =					self.scheduler.step(
												    UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  eta=UpperCamelCase_       ,  use_clipped_model_output=UpperCamelCase_       ,  generator=UpperCamelCase_				).prev_sample

							UpperCamelCase__  :List[Any]				   =					(image / 2 + 0.5).clamp(0       ,  1				)
							UpperCamelCase__  :str				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				).numpy()
							if output_type == "pil":
												UpperCamelCase__  :int				   =					self.numpy_to_pil(UpperCamelCase_				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=UpperCamelCase_				)



'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


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

__snake_case				 =	{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case				 =	{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

__snake_case				 =	{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							VOCAB_FILES_NAMES
		_a       					=							PRETRAINED_VOCAB_FILES_MAP
		_a       					=							PRETRAINED_INIT_CONFIGURATION
		_a       					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a       					=							ElectraTokenizer




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_="[UNK]"       ,  UpperCamelCase_="[SEP]"       ,  UpperCamelCase_="[PAD]"       ,  UpperCamelCase_="[CLS]"       ,  UpperCamelCase_="[MASK]"       ,  UpperCamelCase_=True       ,  UpperCamelCase_=None       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


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

							UpperCamelCase__  :str				   =					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
							if (
							    normalizer_state.get('''lowercase'''       ,  UpperCamelCase_				) != do_lower_case
							    or normalizer_state.get('''strip_accents'''       ,  UpperCamelCase_				) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''       ,  UpperCamelCase_				) != tokenize_chinese_chars
							):
												UpperCamelCase__  :Any				   =					getattr(UpperCamelCase_       ,  normalizer_state.pop('''type'''				)				)
												UpperCamelCase__  :Any				   =					do_lower_case
												UpperCamelCase__  :Dict				   =					strip_accents
												UpperCamelCase__  :Optional[int]				   =					tokenize_chinese_chars
												UpperCamelCase__  :Dict				   =					normalizer_class(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					do_lower_case




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					[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        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					[self.sep_token_id]
							UpperCamelCase__  :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        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self._tokenizer.model.save(UpperCamelCase_       ,  name=UpperCamelCase_				)
							return tuple(UpperCamelCase_				)