




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


_A :   List[str]  			=  logging.get_logger(__name__)

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

_A :   List[Any]  			=  {
    """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"""
        ),
    },
}

_A :   Any  			=  {
    """Salesforce/codegen-350M-mono""": 20_48,
}


class        __SCREAMING_SNAKE_CASE      (			__snake_case ):
       _UpperCAmelCase				:  str			   =   VOCAB_FILES_NAMES
       _UpperCAmelCase				:  Dict			   =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase				:  Tuple			   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase				:  Tuple			   =   ["input_ids", "attention_mask"]
       _UpperCAmelCase				:  Any			   =   CodeGenTokenizer







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

          if kwargs.pop('''add_bos_token'''		, a_      ):
             lowerCamelCase__       :						Any        =							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.'''      )

          lowerCamelCase__       :						Dict        =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
          if pre_tok_state.get('''add_prefix_space'''		, a_      ) != add_prefix_space:
             lowerCamelCase__       :						List[Any]        =							getattr(a_		, pre_tok_state.pop('''type'''      )      )
             lowerCamelCase__       :						int        =							add_prefix_space
             lowerCamelCase__       :						int        =							pre_tok_class(**a_      )

          lowerCamelCase__       :						Union[str, Any]        =							add_prefix_space







       def 						__lowerCamelCase   (				self					:     str		, *A					:     List[Any]		, **A					:     Union[str, Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						Union[str, Any]        =							kwargs.get('''is_split_into_words'''		, a_      )
          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(*a_		, **a_      )







       def 						__lowerCamelCase   (				self					:     int		, *A					:     str		, **A					:     Optional[int]      )       ->List[Any]:
          lowerCamelCase__       :						Optional[int]        =							kwargs.get('''is_split_into_words'''		, a_      )

          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(*a_		, **a_      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Any		, A					:     str = None      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							self._tokenizer.model.save(a_		, name=a_      )
          return tuple(a_      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Optional[int]		, A					:     Dict = False		, A					:     List[str] = None		, A					:     Any = None		, **A					:     List[str]		, )       ->int:
          lowerCamelCase__       :						int        =							super().decode(
              token_ids=a_		, skip_special_tokens=a_		, clean_up_tokenization_spaces=a_		, **a_		, )

          if truncate_before_pattern is not None and len(a_      ) > 0:
             lowerCamelCase__       :						List[Any]        =							self.truncate(a_		, a_      )

          return decoded_text







       def 						__lowerCamelCase   (				self					:     str		, A					:     Any		, A					:     str      )       ->int:

          def find_re(A					:     int		, A					:     str		, A					:     Union[str, Any]      ):
             lowerCamelCase__       :						List[str]        =							pattern.search(a_		, a_      )
             return m.start() if m else -1

          lowerCamelCase__       :						Optional[int]        =							[re.compile(a_		, re.MULTILINE      ) for pattern in truncate_before_pattern]

          lowerCamelCase__       :						Any        =							list(re.finditer('''^print'''		, a_		, re.MULTILINE      )      )

          if len(a_      ) > 1:
             lowerCamelCase__       :						List[str]        =							completion[: prints[1].start()]

          lowerCamelCase__       :						Dict        =							list(re.finditer('''^def'''		, a_		, re.MULTILINE      )      )

          if len(a_      ) > 1:
             lowerCamelCase__       :						Optional[Any]        =							completion[: defs[1].start()]

          lowerCamelCase__       :						Dict        =							0

          lowerCamelCase__       :						List[Any]        =							[
              pos for pos in [find_re(a_		, a_		, a_      ) for terminal in terminals] if pos != -1
          ]

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










import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_A :   Optional[Any]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







       def __init__(				self					:     Optional[Any]		, A					:     AutoencoderKL		, A					:     CLIPTextModel		, A					:     CLIPTokenizer		, A					:     UNetaDConditionModel		, A					:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		, A					:     StableDiffusionSafetyChecker		, A					:     CLIPImageProcessor		, )       ->List[Any]:
          super().__init__()
          self.register_modules(
              vae=A		, text_encoder=A		, tokenizer=A		, unet=A		, scheduler=A		, safety_checker=A		, feature_extractor=A		, )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Optional[Union[str, int]] = "auto"      )       ->Tuple:
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             lowerCamelCase__       :						List[Any]        =							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          self.enable_attention_slicing(A      )







       @torch.no_grad()
       def __call__(				self					:     Union[str, Any]		, A					:     Union[str, List[str]]		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 5_0		, A					:     float = 7.5		, A					:     Optional[Union[str, List[str]]] = None		, A					:     Optional[int] = 1		, A					:     float = 0.0		, A					:     Optional[torch.Generator] = None		, A					:     Optional[torch.FloatTensor] = None		, A					:     Optional[str] = "pil"		, A					:     bool = True		, A					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None		, A					:     int = 1		, A					:     Optional[torch.FloatTensor] = None		, **A					:     int		, )       ->Tuple:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						str        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							len(A      )
          else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A      )}"      )

          if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."      )

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(A		, A      ) or callback_steps <= 0)
          ):
             raise ValueError(
                 F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 F" {type(A      )}."      )

          # get prompt text embeddings
          lowerCamelCase__       :						Optional[Any]        =							self.tokenizer(
              A		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, return_tensors='''pt'''		, )
          lowerCamelCase__       :						Dict        =							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase__       :						Optional[Any]        =							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 F" {self.tokenizer.model_max_length} tokens: {removed_text}"      )
             lowerCamelCase__       :						Any        =							text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             lowerCamelCase__       :						str        =							self.text_encoder(text_input_ids.to(self.device      )      )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						int        =							text_embeddings.shape
          lowerCamelCase__       :						int        =							text_embeddings.repeat(1		, A		, 1      )
          lowerCamelCase__       :						str        =							text_embeddings.view(bs_embed * num_images_per_prompt		, A		, -1      )

          # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
          # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
          # corresponds to doing no classifier free guidance.
          lowerCamelCase__       :						Optional[Any]        =							guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]
             if negative_prompt is None:
                lowerCamelCase__       :						Optional[int]        =							['''''']
             elif type(A      ) is not type(A      ):
                raise TypeError(
                    F"`negative_prompt` should be the same type to `prompt`, but got {type(A      )} !="
                    F" {type(A      )}."      )
             elif isinstance(A		, A      ):
                lowerCamelCase__       :						Optional[int]        =							[negative_prompt]
             elif batch_size != len(A      ):
                raise ValueError(
                    F"`negative_prompt`: {negative_prompt} has batch size {len(A      )}, but `prompt`:"
                    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    ''' the batch size of `prompt`.'''      )
             else:
                lowerCamelCase__       :						List[Any]        =							negative_prompt

             lowerCamelCase__       :						int        =							text_input_ids.shape[-1]
             lowerCamelCase__       :						Optional[int]        =							self.tokenizer(
                 A		, padding='''max_length'''		, max_length=A		, truncation=A		, return_tensors='''pt'''		, )
             lowerCamelCase__       :						Optional[Any]        =							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase__       :						Any        =							uncond_embeddings.shape[1]
             lowerCamelCase__       :						Union[str, Any]        =							uncond_embeddings.repeat(A		, A		, 1      )
             lowerCamelCase__       :						str        =							uncond_embeddings.view(batch_size * num_images_per_prompt		, A		, -1      )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             lowerCamelCase__       :						str        =							torch.cat([uncond_embeddings, text_embeddings]      )

          # get the initial random noise unless the user supplied it

          # Unlike in other pipelines, latents need to be generated in the target device
          # for 1-to-1 results reproducibility with the CompVis implementation.
          # However this currently doesn't work in `mps`.
          lowerCamelCase__       :						Dict        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          lowerCamelCase__       :						List[Any]        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          lowerCamelCase__       :						List[Any]        =							text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not exist on mps
                lowerCamelCase__       :						Dict        =							torch.randn(
                    A		, generator=A		, device='''cpu'''		, dtype=A      ).to(self.device      )
                lowerCamelCase__       :						Dict        =							torch.randn(A		, generator=A		, device='''cpu'''		, dtype=A      ).to(
                    self.device      )
             else:
                lowerCamelCase__       :						Optional[Any]        =							torch.randn(
                    A		, generator=A		, device=self.device		, dtype=A      )
                lowerCamelCase__       :						Union[str, Any]        =							torch.randn(A		, generator=A		, device=self.device		, dtype=A      )
          else:
             if latents_reference.shape != latents_shape:
                raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"      )
             lowerCamelCase__       :						Optional[Any]        =							latents_reference.to(self.device      )
             lowerCamelCase__       :						Optional[int]        =							latents.to(self.device      )

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          lowerCamelCase__       :						List[str]        =							(latents_shape[3] - latents_shape_reference[3]) // 2
          lowerCamelCase__       :						List[Any]        =							(latents_shape[2] - latents_shape_reference[2]) // 2
          lowerCamelCase__       :						Dict        =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          lowerCamelCase__       :						str        =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          lowerCamelCase__       :						int        =							0 if dx < 0 else dx
          lowerCamelCase__       :						Optional[int]        =							0 if dy < 0 else dy
          lowerCamelCase__       :						Dict        =							max(-dx		, 0      )
          lowerCamelCase__       :						int        =							max(-dy		, 0      )
          # import pdb
          # pdb.set_trace()
          lowerCamelCase__       :						str        =							latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(A      )

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps.to(self.device      )

          # scale the initial noise by the standard deviation required by the scheduler
          lowerCamelCase__       :						List[Any]        =							latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          lowerCamelCase__       :						Union[str, Any]        =							'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
          lowerCamelCase__       :						List[Any]        =							{}
          if accepts_eta:
             lowerCamelCase__       :						Any        =							eta

          for i, t in enumerate(self.progress_bar(A      )      ):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase__       :						Optional[int]        =							torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
             lowerCamelCase__       :						Any        =							self.scheduler.scale_model_input(A		, A      )

             # predict the noise residual
             lowerCamelCase__       :						Union[str, Any]        =							self.unet(A		, A		, encoder_hidden_states=A      ).sample

             # perform guidance
             if do_classifier_free_guidance:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							noise_pred.chunk(2      )
                lowerCamelCase__       :						int        =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__       :						Dict        =							self.scheduler.step(A		, A		, A		, **A      ).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                callback(A		, A		, A      )

          lowerCamelCase__       :						Optional[Any]        =							1 / 0.1_82_15 * latents
          lowerCamelCase__       :						int        =							self.vae.decode(A      ).sample

          lowerCamelCase__       :						Dict        =							(image / 2 + 0.5).clamp(0		, 1      )

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          lowerCamelCase__       :						List[str]        =							image.cpu().permute(0		, 2		, 3		, 1      ).float().numpy()

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							self.feature_extractor(self.numpy_to_pil(A      )		, return_tensors='''pt'''      ).to(
                 self.device      )
             lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.safety_checker(
                 images=A		, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
          else:
             lowerCamelCase__       :						List[Any]        =							None

          if output_type == "pil":
             lowerCamelCase__       :						Optional[Any]        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A		, nsfw_content_detected=A      )



