

from typing import List, Optional, Tuple, Union

import torch

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



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			UNetaDModel
  lowerCamelCase    :			PNDMScheduler







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    UNetaDModel       ,							lowerCAmelCase :    PNDMScheduler    )							->	Dict:






     """simple docstring"""







     super().__init__()

     __lowerCAmelCase  :			Union[str, Any]					    =						PNDMScheduler.from_config(scheduler.config    )

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







  @torch.no_grad()
  def __call__(			self :    Optional[Any]       ,							lowerCAmelCase :    int = 1       ,							lowerCAmelCase :    int = 50       ,							lowerCAmelCase :    Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							lowerCAmelCase :    Optional[str] = "pil"       ,							lowerCAmelCase :    bool = True       ,							**lowerCAmelCase :    Dict       ,							)							->	Union[ImagePipelineOutput, Tuple]:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,							generator=lowerCAmelCase       ,							device=self.device       ,							)

     self.scheduler.set_timesteps(lowerCAmelCase    )
     for t in self.progress_bar(self.scheduler.timesteps    ):
        __lowerCAmelCase  :			int					    =						self.unet(lowerCAmelCase       ,							lowerCAmelCase    ).sample

        __lowerCAmelCase  :			Any					    =						self.scheduler.step(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    ).prev_sample

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase    )




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							lowerCamelCase_  :       Optional[Any]					      =     None


lowerCamelCase_  :       str					      =     logging.get_logger(__name__)

lowerCamelCase_  :       List[str]					      =     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_  :       List[str]					      =     {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_  :       Dict					      =     {
    """facebook/xglm-564M""": 20_48,
}

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[Any]    					=       VOCAB_FILES_NAMES
			__UpperCamelCase					:  int    					=       PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase					:  List[str]    					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase					:  Dict    					=       ["""input_ids""", """attention_mask"""]
			__UpperCamelCase					:  Union[str, Any]    					=       XGLMTokenizer

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=None  ,		snake_case_       :				Any=None  ,		snake_case_       :				Optional[Any]="<s>"  ,		snake_case_       :				Union[str, Any]="</s>"  ,		snake_case_       :				Optional[Any]="</s>"  ,		snake_case_       :				Dict="<s>"  ,		snake_case_       :				int="<unk>"  ,		snake_case_       :				Dict="<pad>"  ,		**snake_case_       :				List[str]  ,		):
							# Compatibility with the original tokenizer
							UpperCamelCase_:							List[Any]		=  7
							UpperCamelCase_:							int		=  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

							UpperCamelCase_:							Any		=  kwargs.get("""additional_special_tokens"""  ,		[]					)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    snake_case_  ,		tokenizer_file=snake_case_  ,		bos_token=snake_case_  ,		eos_token=snake_case_  ,		sep_token=snake_case_  ,		cls_token=snake_case_  ,		unk_token=snake_case_  ,		pad_token=snake_case_  ,		**snake_case_  ,		)

							UpperCamelCase_:							Dict		=  vocab_file
							UpperCamelCase_:							Union[str, Any]		=  False if not self.vocab_file else True

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):

							if token_ids_a is None:
											return [self.sep_token_id] + token_ids_a
							UpperCamelCase_:							Optional[Any]		=  [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				List[int]  ,		snake_case_       :				Optional[List[int]] = None					):
							UpperCamelCase_:							Any		=  [self.sep_token_id]

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




			def 					lowerCAmelCase__		(				self       :				Optional[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Optional[str] = None					):
							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
											    """tokenizer."""					)

							if not os.path.isdir(snake_case_					):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''					)
											return
							UpperCamelCase_:							Optional[int]		=  os.path.join(
							    snake_case_  ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case_					):
											copyfile(self.vocab_file  ,		snake_case_					)

							return (out_vocab_file,)






