




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       List[Any]							=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__						:       Tuple							=    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

UpperCAmelCase__						:       Dict							=    {
    'openai-gpt': 512,
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							str							=					VOCAB_FILES_NAMES
   __SCREAMING_SNAKE_CASE		:							Dict							=					PRETRAINED_VOCAB_FILES_MAP
   __SCREAMING_SNAKE_CASE		:							Dict							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __SCREAMING_SNAKE_CASE		:							str							=					["input_ids", "attention_mask"]
   __SCREAMING_SNAKE_CASE		:							str							=					OpenAIGPTTokenizer






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Union[str, Any]=None						,					UpperCamelCase_   :       Optional[Any]=None						,					UpperCamelCase_   :       List[Any]=None						,					UpperCamelCase_   :       Tuple="<unk>"						,					**UpperCamelCase_   :       Union[str, Any]     )->			Any:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_						,					tokenizer_file=UpperCamelCase_						,					unk_token=UpperCamelCase_						,					**UpperCamelCase_     )






   @property
   def  _a						(	self   :       Any     )->			int:
     return True






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[str] = None     )->			Tuple[str]:
     _lowercase: Optional[Any]						     =    self._tokenizer.model.save(UpperCamelCase_						,					name=UpperCamelCase_     )
     return tuple(UpperCamelCase_     )






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

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

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    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
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

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

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
