



'''simple docstring'''







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			(	A__	):
    """simple docstring"""
    a													=   ["vqvae"]


    def __init__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							AutoencoderKL				,							SCREAMING_SNAKE_CASE      :							UNetaDConditionModel				,							SCREAMING_SNAKE_CASE      :							Mel				,							SCREAMING_SNAKE_CASE      :							Union[DDIMScheduler, DDPMScheduler]				,							):
           super().__init__()
           self.register_modules(unet=__snake_case				,							scheduler=__snake_case				,							mel=__snake_case				,							vqvae=__snake_case)


    def        A   (  self      :							Union[str, Any]):
           return 50 if isinstance(self.scheduler				,							__snake_case) else 1000


    @torch.no_grad()
    def __call__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							np.ndarray = None				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = None				,							SCREAMING_SNAKE_CASE      :							torch.Generator = None				,							SCREAMING_SNAKE_CASE      :							float = 0				,							SCREAMING_SNAKE_CASE      :							float = 0				,							SCREAMING_SNAKE_CASE      :							torch.Generator = None				,							SCREAMING_SNAKE_CASE      :							float = 0				,							SCREAMING_SNAKE_CASE      :							torch.Tensor = None				,							SCREAMING_SNAKE_CASE      :							torch.Tensor = None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=True				,							):
           _A					:     Any     = steps or self.get_default_steps()
           self.scheduler.set_timesteps(__snake_case)
           _A					:     Dict     = step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size) == int:
                  _A					:     Union[str, Any]     = (self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                  _A					:     Any     = randn_tensor(
                      (
                          batch_size,
                          self.unet.config.in_channels,
                          self.unet.config.sample_size[0],
                          self.unet.config.sample_size[1],
                      )				,							generator=__snake_case				,							device=self.device				,							)
           _A					:     str     = noise
           _A					:     Dict     = None

           if audio_file is not None or raw_audio is not None:
                  self.mel.load_audio(__snake_case				,							__snake_case)
                  _A					:     Tuple     = self.mel.audio_slice_to_image(__snake_case)
                  _A					:     Tuple     = np.frombuffer(input_image.tobytes()				,							dtype='uint8').reshape(
                      (input_image.height, input_image.width))
                  _A					:     List[str]     = (input_image / 255) * 2 - 1
                  _A					:     Tuple     = torch.tensor(input_image[np.newaxis, :, :]				,							dtype=torch.float).to(self.device)

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

                  if start_step > 0:
                         _A					:     Any     = self.scheduler.add_noise(__snake_case				,							__snake_case				,							self.scheduler.timesteps[start_step - 1])

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

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
                  if isinstance(self.unet				,							__snake_case):
                         _A					:     Dict     = self.unet(__snake_case				,							__snake_case				,							__snake_case)['sample']
                  else:
                         _A					:     Optional[Any]     = self.unet(__snake_case				,							__snake_case)['sample']

                  if isinstance(self.scheduler				,							__snake_case):
                         _A					:     Optional[int]     = self.scheduler.step(
                             model_output=__snake_case				,							timestep=__snake_case				,							sample=__snake_case				,							eta=__snake_case				,							generator=__snake_case				,							)['prev_sample']
                  else:
                         _A					:     List[str]     = self.scheduler.step(
                             model_output=__snake_case				,							timestep=__snake_case				,							sample=__snake_case				,							generator=__snake_case				,							)['prev_sample']

                  if mask is not None:
                         if mask_start > 0:
                                _A					:     int     = mask[:, step, :, :mask_start]
                         if mask_end > 0:
                                _A					:     Optional[Any]     = mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                  # 0.18215 was scaling factor used in training to ensure unit variance
                  _A					:     Optional[int]     = 1 / self.vqvae.config.scaling_factor * images
                  _A					:     Optional[Any]     = self.vqvae.decode(__snake_case)['sample']

           _A					:     str     = (images / 2 + 0.5).clamp(0				,							1)
           _A					:     Union[str, Any]     = images.cpu().permute(0				,							2				,							3				,							1).numpy()
           _A					:     Any     = (images * 255).round().astype('uint8')
           _A					:     Dict     = list(
               (Image.fromarray(_[:, :, 0]) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(__snake_case				,							mode='RGB').convert('L') for _ in images))

           _A					:     Any     = [self.mel.image_to_audio(__snake_case) for _ in images]
           if not return_dict:
                  return images, (self.mel.get_sample_rate(), audios)

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


    @torch.no_grad()
    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							List[Image.Image]				,							SCREAMING_SNAKE_CASE      :							int = 50):
           assert isinstance(self.scheduler				,							__snake_case)
           self.scheduler.set_timesteps(__snake_case)
           _A					:     Optional[int]     = np.array(
               [np.frombuffer(image.tobytes()				,							dtype='uint8').reshape((1, image.height, image.width)) for image in images])
           _A					:     Dict     = (sample / 255) * 2 - 1
           _A					:     List[str]     = torch.Tensor(__snake_case).to(self.device)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,							(0,))):
                  _A					:     List[str]     = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                  _A					:     Any     = 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					:     Dict     = self.unet(__snake_case				,							__snake_case)['sample']
                  _A					:     List[str]     = (1 - alpha_prod_t_prev) ** 0.5 * model_output
                  _A					:     Dict     = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                  _A					:     List[str]     = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample


    @staticmethod
    def        A   (  SCREAMING_SNAKE_CASE      :							torch.Tensor				,							SCREAMING_SNAKE_CASE      :							torch.Tensor				,							SCREAMING_SNAKE_CASE      :							float):
           _A					:     Tuple     = acos(torch.dot(torch.flatten(__snake_case)				,							torch.flatten(__snake_case)) / torch.norm(__snake_case) / torch.norm(__snake_case))
           return sin((1 - alpha) * theta) * xa / sin(__snake_case) + sin(alpha * theta) * xa / sin(__snake_case)










'''simple docstring'''







import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A				:					int    						=			2


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[str]				,							*,  # begin keyword-only arguments
        SCREAMING_SNAKE_CASE      :							Optional[Any]="<s>"				,							SCREAMING_SNAKE_CASE      :							int="<pad>"				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="</s>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<unk>"				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							):
           _A  ,			_A  ,			_A  ,			_A					:     Any     = bos, unk, pad, eos
           _A					:     Optional[Any]     = []
           _A					:     Optional[Any]     = []
           _A					:     Optional[int]     = {}
           _A					:     Dict     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     str     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     Any     = self.add_symbol(SCREAMING_SNAKE_CASE)
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = len(self.symbols)


    def __eq__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.indices == other.indices


    def __getitem__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word


    def __len__(  self      :							Union[str, Any]):
           return len(self.symbols)


    def __contains__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return sym in self.indices


    @classmethod
    def        A   (  cls      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = cls()
           d.add_from_file(SCREAMING_SNAKE_CASE)
           return d


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							int=False):
           if word in self.indices and not overwrite:
                  _A					:     str     = self.indices[word]
                  _A					:     List[str]     = self.count[idx] + n
                  return idx
           else:
                  _A					:     Optional[Any]     = len(self.symbols)
                  _A					:     Union[str, Any]     = idx
                  self.symbols.append(SCREAMING_SNAKE_CASE)
                  self.count.append(SCREAMING_SNAKE_CASE)
                  return idx


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return 0


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  try:
                         with open(SCREAMING_SNAKE_CASE				,							'r'				,							encoding='utf-8') as fd:
                                self.add_from_file(SCREAMING_SNAKE_CASE)
                  except FileNotFoundError as fnfe:
                         raise fnfe
                  except UnicodeError:
                         raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE))
                  return

           _A					:     Union[str, Any]     = f.readlines()
           _A					:     Any     = self._load_meta(SCREAMING_SNAKE_CASE)

           for line in lines[indices_start_line:]:
                  try:
                         _A  ,			_A					:     List[str]     = line.rstrip().rsplit(' '				,							1)
                         if field == "#fairseq:overwrite":
                                _A					:     int     = True
                                _A  ,			_A					:     List[str]     = line.rsplit(' '				,							1)
                         else:
                                _A					:     Union[str, Any]     = False
                         _A					:     List[str]     = int(SCREAMING_SNAKE_CASE)
                         _A					:     Optional[Any]     = line
                         if word in self and not overwrite:
                                raise RuntimeError(
                                    'Duplicate word found when loading Dictionary: \'{}\'. '
                                    'Duplicate words can overwrite earlier ones by adding the '
                                    '#fairseq:overwrite flag at the end of the corresponding row '
                                    'in the dictionary file. If using the Camembert model, please '
                                    'download an updated copy of the model file.'.format(SCREAMING_SNAKE_CASE))
                         self.add_symbol(SCREAMING_SNAKE_CASE				,							n=SCREAMING_SNAKE_CASE				,							overwrite=SCREAMING_SNAKE_CASE)
                  except ValueError:
                         raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       _A					:     Union[str, Any]     = dict((re.sub(R'@@$'  ,''  ,lowerCamelCase       ), v) if k.endswith('@@'       ) else (re.sub(R'$'  ,'</w>'  ,lowerCamelCase       ), v) for k, v in d.items()       )
       _A					:     Optional[Any]     = '<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              _A					:     str     = d[k]  # restore
       return da






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]       ):
       # prep
       if not os.path.exists(lowerCamelCase       ):
              raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'       )
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       print(F'Writing results to {pytorch_dump_folder_path}'       )

       # handle various types of models

       _A					:     Dict     = os.path.join(lowerCamelCase  ,'checkpoint.pt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {checkpoint_file} does not exist!'       )
       _A					:     int     = torch.load(lowerCamelCase  ,map_location='cpu'       )

       _A					:     Dict     = chkpt['cfg']['model']

       # dicts
       _A					:     Any     = os.path.join(lowerCamelCase  ,'dict.txt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {dict_file} does not exist!'       )
       _A					:     Any     = Dictionary.load(lowerCamelCase       )
       _A					:     Optional[int]     = rewrite_dict_keys(src_dict.indices       )
       _A					:     List[Any]     = len(lowerCamelCase       )
       _A					:     str     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['vocab_file']       )
       print(F'Generating {src_vocab_file} of {src_vocab_size} records'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # merges_file (bpecodes)
       _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,'bpecodes'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

       _A					:     Dict     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['merges_file']       )
       shutil.copyfile(lowerCamelCase  ,lowerCamelCase       )

       # model config
       _A					:     str     = os.path.join(lowerCamelCase  ,'config.json'       )

       _A					:     int     = {
           'activation_dropout': args['activation_dropout'],
           'architectures': ['BioGptForCausalLM'],
           'attention_probs_dropout_prob': args['attention_dropout'],
           'bos_token_id': 0,
           'eos_token_id': 2,
           'hidden_act': args['activation_fn'],
           'hidden_dropout_prob': args['dropout'],
           'hidden_size': args['decoder_embed_dim'],
           'initializer_range': 0.02,
           'intermediate_size': args['decoder_ffn_embed_dim'],
           'layer_norm_eps': 1E-12,
           'layerdrop': args['decoder_layerdrop'],
           'max_position_embeddings': args['max_target_positions'],
           'model_type': 'biogpt',
           'num_attention_heads': args['decoder_attention_heads'],
           'num_hidden_layers': args['decoder_layers'],
           'pad_token_id': 1,
           'scale_embedding': not args['no_scale_embedding'],
           'tie_word_embeddings': args['share_decoder_input_output_embed'],
           'vocab_size': src_vocab_size,
       }

       # good hparam defaults to start with

       print(F'Generating {biogpt_model_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # tokenizer config
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = {
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

       print(F'Generating {biogpt_tokenizer_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # model
       _A					:     List[Any]     = chkpt['model']

       # remove unneeded keys
       _A					:     int     = [
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = list(model_state_dict.keys()       )
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'       ):
                     _A					:     str     = model_state_dict.pop(lowerCamelCase       )
              else:
                     _A					:     Dict     = model_state_dict.pop(lowerCamelCase       )

       _A					:     Any     = BioGptConfig.from_pretrained(lowerCamelCase       )
       _A					:     Union[str, Any]     = BioGptForCausalLM(lowerCamelCase       )

       # check that it loads ok
       model_new.load_state_dict(lowerCamelCase       )

       # save
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
       print(F'Generating {pytorch_weights_dump_path}'       )
       torch.save(lowerCamelCase  ,lowerCamelCase       )

       print('Conversion is done!'       )


if __name__ == "__main__":
   A				:					Optional[Any]    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--biogpt_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
           ''' bpecodes, etc.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   A				:					int    						=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






