





'''simple docstring'''




import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


__SCREAMING_SNAKE_CASE     :str       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Tuple       =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE     :int       =				{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

__SCREAMING_SNAKE_CASE     :Tuple       =				{
    '''Salesforce/codegen-350M-mono''': 2048,
}







class   A_	(  __lowerCAmelCase			):
 _lowerCamelCase  : Union[str, Any]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : List[Any]           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Any           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : Optional[Any]           =							["""input_ids""", """attention_mask"""]
 _lowerCamelCase  : List[str]           =							CodeGenTokenizer




 def __init__(					self       :    Any	,			snake_case_       :    Union[str, Any]=None	,			snake_case_       :    List[Any]=None	,			snake_case_       :    str=None	,			snake_case_       :    Dict="<|endoftext|>"	,			snake_case_       :    Dict="<|endoftext|>"	,			snake_case_       :    Dict="<|endoftext|>"	,			snake_case_       :    Tuple=False	,			**snake_case_       :    int	,			):
      super().__init__(
          snake_case_	,			snake_case_	,			tokenizer_file=snake_case_	,			unk_token=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			add_prefix_space=snake_case_	,			**snake_case_	,			)

      if kwargs.pop("add_bos_token"	,			snake_case_    ):
           _UpperCAmelCase     							=   kwargs.pop("name_or_path"	,			""    )
           raise ValueError(
               "Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token."
               "Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n"
               f'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
               f'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
               "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
               " so that the fast tokenizer works correctly."    )

      _UpperCAmelCase     							=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
      if pre_tok_state.get("add_prefix_space"	,			snake_case_    ) != add_prefix_space:
           _UpperCAmelCase     							=   getattr(snake_case_	,			pre_tok_state.pop("type"    )    )
           _UpperCAmelCase     							=   add_prefix_space
           _UpperCAmelCase     							=   pre_tok_class(**snake_case_    )

      _UpperCAmelCase     							=   add_prefix_space




 def      lowercase	(					self       :    Any	,			*snake_case_       :    int	,			**snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   kwargs.get("is_split_into_words"	,			snake_case_    )
      assert self.add_prefix_space or not is_split_into_words, (
          f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			*snake_case_       :    Optional[int]	,			**snake_case_       :    Any    ):
      _UpperCAmelCase     							=   kwargs.get("is_split_into_words"	,			snake_case_    )

      assert self.add_prefix_space or not is_split_into_words, (
          f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    int	,			snake_case_       :    Dict = None    ):
      _UpperCAmelCase     							=   self._tokenizer.model.save(snake_case_	,			name=snake_case_    )
      return tuple(snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]	,			snake_case_       :    List[Any] = False	,			snake_case_       :    str = None	,			snake_case_       :    Tuple = None	,			**snake_case_       :    Union[str, Any]	,			):
      _UpperCAmelCase     							=   super().decode(
          token_ids=snake_case_	,			skip_special_tokens=snake_case_	,			clean_up_tokenization_spaces=snake_case_	,			**snake_case_	,			)

      if truncate_before_pattern is not None and len(snake_case_    ) > 0:
           _UpperCAmelCase     							=   self.truncate(snake_case_	,			snake_case_    )

      return decoded_text




 def      lowercase	(					self       :    int	,			snake_case_       :    int	,			snake_case_       :    Union[str, Any]    ):





      def find_re(snake_case_       :    Tuple	,			snake_case_       :    List[str]	,			snake_case_       :    str    ):
           _UpperCAmelCase     							=   pattern.search(snake_case_	,			snake_case_    )
           return m.start() if m else -1

      _UpperCAmelCase     							=   [re.compile(snake_case_	,			re.MULTILINE    ) for pattern in truncate_before_pattern]

      _UpperCAmelCase     							=   list(re.finditer("^print"	,			snake_case_	,			re.MULTILINE    )    )

      if len(snake_case_    ) > 1:
           _UpperCAmelCase     							=   completion[: prints[1].start()]

      _UpperCAmelCase     							=   list(re.finditer("^def"	,			snake_case_	,			re.MULTILINE    )    )

      if len(snake_case_    ) > 1:
           _UpperCAmelCase     							=   completion[: defs[1].start()]

      _UpperCAmelCase     							=   0

      _UpperCAmelCase     							=   [
          pos for pos in [find_re(snake_case_	,			snake_case_	,			snake_case_    ) for terminal in terminals] if pos != -1
      ]

      if len(snake_case_    ) > 0:
           return completion[: min(snake_case_    )]
      else:
           return completion





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

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

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

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



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

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

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).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
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


