






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


UpperCAmelCase     :     Tuple							=	logging.get_logger(__name__)

UpperCAmelCase     :     int							=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase     :     List[str]							=	{
    """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"""
        ),
    },
}

UpperCAmelCase     :     List[str]							=	{
    """Salesforce/codegen-350M-mono""": 2048,
}







class     __lowerCAmelCase	(   a_):
      _lowercase    : str            =      VOCAB_FILES_NAMES
      _lowercase    : Union[str, Any]            =      PRETRAINED_VOCAB_FILES_MAP
      _lowercase    : Any            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase    : Tuple            =      ['''input_ids''', '''attention_mask''']
      _lowercase    : int            =      CodeGenTokenizer







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__="<|endoftext|>"		,				lowerCAmelCase__=False		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowercase_		,				lowercase_		,				tokenizer_file=lowercase_		,				unk_token=lowercase_		,				bos_token=lowercase_		,				eos_token=lowercase_		,				add_prefix_space=lowercase_		,				**lowercase_		,				)

        if kwargs.pop("add_bos_token"		,				lowercase_							):
          a__     : Optional[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."							)

        a__     : List[str]  =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"		,				lowercase_							) != add_prefix_space:
          a__     : List[Any]  =getattr(lowercase_		,				pre_tok_state.pop("type"							)							)
          a__     : Optional[Any]  =add_prefix_space
          a__     : Dict  =pre_tok_class(**lowercase_							)

        a__     : Tuple  =add_prefix_space







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						BatchEncoding:



        '''simple docstring'''




        a__     : List[str]  =kwargs.get("is_split_into_words"		,				lowercase_							)
        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(*lowercase_		,				**lowercase_							)







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						BatchEncoding:



        '''simple docstring'''




        a__     : int  =kwargs.get("is_split_into_words"		,				lowercase_							)

        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(*lowercase_		,				**lowercase_							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						Tuple[str]:



        '''simple docstring'''




        a__     : Any  =self._tokenizer.model.save(lowercase_		,				name=lowercase_							)
        return tuple(lowercase_							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        a__     : Any  =super().decode(
            token_ids=lowercase_		,				skip_special_tokens=lowercase_		,				clean_up_tokenization_spaces=lowercase_		,				**lowercase_		,				)

        if truncate_before_pattern is not None and len(lowercase_							) > 0:
          a__     : Tuple  =self.truncate(lowercase_		,				lowercase_							)

        return decoded_text







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        def find_re(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							):
          a__     : Optional[Any]  =pattern.search(lowercase_		,				lowercase_							)
          return m.start() if m else -1

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

        a__     : List[Any]  =list(re.finditer("^print"		,				lowercase_		,				re.MULTILINE							)							)

        if len(lowercase_							) > 1:
          a__     : int  =completion[: prints[1].start()]

        a__     : Union[str, Any]  =list(re.finditer("^def"		,				lowercase_		,				re.MULTILINE							)							)

        if len(lowercase_							) > 1:
          a__     : Union[str, Any]  =completion[: defs[1].start()]

        a__     : int  =0

        a__     : List[str]  =[
            pos for pos in [find_re(lowercase_		,				lowercase_		,				lowercase_							) for terminal in terminals] if pos != -1
        ]

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













from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      ["""vqvae"""]







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Union[str, Any]:



        '''simple docstring'''




        super().__init__()
        self.register_modules(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__		,				mel=lowerCAmelCase__		,				vqvae=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return 5_0 if isinstance(self.scheduler		,				lowerCAmelCase__							) else 1_0_0_0







      @torch.no_grad()
      def __call__(   self		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__=True		,				)       ->						Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:



        '''simple docstring'''




        a__     : List[Any]  =steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Tuple  =step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size							) == int:
          a__     : List[str]  =(self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
          a__     : Optional[Any]  =randn_tensor(
              (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size[0],
                  self.unet.config.sample_size[1],
              )		,				generator=lowerCAmelCase__		,				device=self.device		,				)
        a__     : List[str]  =noise
        a__     : Optional[Any]  =None

        if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Tuple  =self.mel.audio_slice_to_image(lowerCAmelCase__							)
          a__     : List[Any]  =np.frombuffer(input_image.tobytes()		,				dtype="uint8"							).reshape(
              (input_image.height, input_image.width)							)
          a__     : Optional[Any]  =(input_image / 2_5_5) * 2 - 1
          a__     : Dict  =torch.tensor(input_image[np.newaxis, :, :]		,				dtype=torch.float							).to(self.device							)

          if self.vqvae is not None:
            a__     : str  =self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__		,				0							)							).latent_dist.sample(
                generator=lowerCAmelCase__							)[0]
            a__     : Any  =self.vqvae.config.scaling_factor * input_images

          if start_step > 0:
            a__     : Optional[int]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				self.scheduler.timesteps[start_step - 1]							)

          a__     : Tuple  =(
              self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
          )
          a__     : Union[str, Any]  =int(mask_start_secs * pixels_per_second							)
          a__     : List[str]  =int(mask_end_secs * pixels_per_second							)
          a__     : Optional[Any]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				torch.tensor(self.scheduler.timesteps[start_step:]							)							)

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
          if isinstance(self.unet		,				lowerCAmelCase__							):
            a__     : List[str]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          else:
            a__     : Optional[Any]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]

          if isinstance(self.scheduler		,				lowerCAmelCase__							):
            a__     : int  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				eta=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]
          else:
            a__     : str  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]

          if mask is not None:
            if mask_start > 0:
              a__     : List[Any]  =mask[:, step, :, :mask_start]
            if mask_end > 0:
              a__     : Union[str, Any]  =mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
          # 0.18215 was scaling factor used in training to ensure unit variance
          a__     : Any  =1 / self.vqvae.config.scaling_factor * images
          a__     : str  =self.vqvae.decode(lowerCAmelCase__							)["sample"]

        a__     : str  =(images / 2 + 0.5).clamp(0		,				1							)
        a__     : int  =images.cpu().permute(0		,				2		,				3		,				1							).numpy()
        a__     : List[Any]  =(images * 2_5_5).round().astype("uint8"							)
        a__     : Dict  =list(
            (Image.fromarray(_[:, :, 0]							) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__		,				mode="RGB"							).convert("L"							) for _ in images)							)

        a__     : str  =[self.mel.image_to_audio(lowerCAmelCase__							) for _ in images]
        if not return_dict:
          return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__							)[:, np.newaxis, :]							)		,				**ImagePipelineOutput(lowerCAmelCase__							)							)







      @torch.no_grad()
      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = 5_0							)       ->						np.ndarray:



        '''simple docstring'''




        assert isinstance(self.scheduler		,				lowerCAmelCase__							)
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Union[str, Any]  =np.array(
            [np.frombuffer(image.tobytes()		,				dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
        a__     : Tuple  =(sample / 2_5_5) * 2 - 1
        a__     : List[Any]  =torch.Tensor(lowerCAmelCase__							).to(self.device							)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,				(0,)							)							):
          a__     : str  =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          a__     : Dict  =self.scheduler.alphas_cumprod[t]
          a__     : Optional[Any]  =(
              self.scheduler.alphas_cumprod[prev_timestep]
              if prev_timestep >= 0
              else self.scheduler.final_alpha_cumprod
          )
          a__     : Optional[Any]  =1 - alpha_prod_t
          a__     : str  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          a__     : Optional[Any]  =(1 - alpha_prod_t_prev) ** 0.5 * model_output
          a__     : List[str]  =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          a__     : Optional[Any]  =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







      @staticmethod
      def        _lowercase  (   lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						torch.Tensor:



        '''simple docstring'''




        a__     : Any  =acos(torch.dot(torch.flatten(lowerCAmelCase__							)		,				torch.flatten(lowerCAmelCase__							)							) / torch.norm(lowerCAmelCase__							) / torch.norm(lowerCAmelCase__							)							)
        return sin((1 - alpha) * theta							) * xa / sin(lowerCAmelCase__							) + sin(alpha * theta							) * xa / sin(lowerCAmelCase__							)






