
"""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()

SCREAMING_SNAKE_CASE_    					=						2
class      _UpperCAmelCase							:




      def __init__(							self   ,       *,  # begin keyword-only arguments
          lowercase_="<s>"   ,       lowercase_="<pad>"   ,       lowercase_="</s>"   ,       lowercase_="<unk>"   ,       lowercase_=None   ,       )						->					str:
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					bos, unk, pad, eos
          UpperCAmelCase									=					[]
          UpperCAmelCase									=					[]
          UpperCAmelCase									=					{}
          UpperCAmelCase									=					self.add_symbol(lowercase_							)
          UpperCAmelCase									=					self.add_symbol(lowercase_							)
          UpperCAmelCase									=					self.add_symbol(lowercase_							)
          UpperCAmelCase									=					self.add_symbol(lowercase_							)
          if extra_special_symbols:
              for s in extra_special_symbols:
                  self.add_symbol(lowercase_							)
          UpperCAmelCase									=					len(self.symbols							)




      def __eq__(							self   ,       lowercase_							)						->					Any:
          return self.indices == other.indices




      def __getitem__(							self   ,       lowercase_							)						->					int:
          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   ,       lowercase_							)						->					int:
          return sym in self.indices




      @classmethod
      def     a_      (							cls   ,       lowercase_							)						->					int:
          UpperCAmelCase									=					cls()
          d.add_from_file(lowercase_							)
          return d




      def     a_      (							self   ,       lowercase_   ,       lowercase_=1   ,       lowercase_=False							)						->					Union[str, Any]:
          if word in self.indices and not overwrite:
              UpperCAmelCase									=					self.indices[word]
              UpperCAmelCase									=					self.count[idx] + n
              return idx
          else:
              UpperCAmelCase									=					len(self.symbols							)
              UpperCAmelCase									=					idx
              self.symbols.append(lowercase_							)
              self.count.append(lowercase_							)
              return idx




      def     a_      (							self   ,       lowercase_							)						->					Union[str, Any]:
          return 0




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

          UpperCAmelCase									=					f.readlines()
          UpperCAmelCase									=					self._load_meta(lowercase_							)

          for line in lines[indices_start_line:]:
              try:
                  UpperCAmelCase			,							UpperCAmelCase									=					line.rstrip().rsplit(' '   ,       1							)
                  if field == "#fairseq:overwrite":
                      UpperCAmelCase									=					True
                      UpperCAmelCase			,							UpperCAmelCase									=					line.rsplit(' '   ,       1							)
                  else:
                      UpperCAmelCase									=					False
                  UpperCAmelCase									=					int(lowercase_							)
                  UpperCAmelCase									=					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(lowercase_							)							)
                  self.add_symbol(lowercase_   ,       n=lowercase_   ,       overwrite=lowercase_							)
              except ValueError:
                  raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''							)




def     lowercase__						(					lowerCAmelCase				:					Dict ) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					dict((re.sub(r'@@$' ,				'' ,				lowerCAmelCase ), v) if k.endswith('@@' ) else (re.sub(r'$' ,				'</w>' ,				lowerCAmelCase ), v) for k, v in d.items() )
    UpperCAmelCase									=					'<s> <pad> </s> <unk>'.split()
    # restore the special tokens
    for k in keep_keys:
        del da[F"{k}</w>"]
        UpperCAmelCase									=					d[k]  # restore
    return da




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Optional[Any] ) ->      int:






    """simple docstring"""




    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

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

    UpperCAmelCase									=					chkpt['cfg']['model']

    # dicts
    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				'dict.txt' )
    if not os.path.isfile(lowerCAmelCase ):
        raise ValueError(F"path to the file {dict_file} does not exist!" )
    UpperCAmelCase									=					Dictionary.load(lowerCAmelCase )
    UpperCAmelCase									=					rewrite_dict_keys(src_dict.indices )
    UpperCAmelCase									=					len(lowerCAmelCase )
    UpperCAmelCase									=					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)
    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				'bpecodes' )
    if not os.path.isfile(lowerCAmelCase ):
        raise ValueError(F"path to the file {bpecodes_file} does not exist!" )

    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				VOCAB_FILES_NAMES['merges_file'] )
    shutil.copyfile(lowerCAmelCase ,				lowerCAmelCase )

    # model config
    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				'config.json' )

    UpperCAmelCase									=					{
        '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-1_2,
        '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
    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				lowerCAmelCase )

    UpperCAmelCase									=					{
        'bos_token': '<s>',
        'eos_token': '</s>',
        'model_max_length': 1_024,
        '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
    UpperCAmelCase									=					chkpt['model']

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

    UpperCAmelCase									=					list(model_state_dict.keys() )
    for layer_name in layer_names:
        if layer_name.endswith('output_projection.weight' ):
            UpperCAmelCase									=					model_state_dict.pop(lowerCAmelCase )
        else:
            UpperCAmelCase									=					model_state_dict.pop(lowerCAmelCase )

    UpperCAmelCase									=					BioGptConfig.from_pretrained(lowerCAmelCase )
    UpperCAmelCase									=					BioGptForCausalLM(lowerCAmelCase )

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

    # save
    UpperCAmelCase									=					os.path.join(lowerCAmelCase ,				lowerCAmelCase )
    print(F"Generating {pytorch_weights_dump_path}" )
    torch.save(lowerCAmelCase ,				lowerCAmelCase )

    print('Conversion is done!' )


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						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.'''
   )
   SCREAMING_SNAKE_CASE_    					=						parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





"""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


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




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[str]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return image
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[image]

    UpperCAmelCase									=					[trans(img.convert('RGB' ) ) for img in image]
    UpperCAmelCase									=					torch.stack(lowerCAmelCase )
    return image







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[str]:
          super().__init__()

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

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




      def     a_      (							self   ,       lowercase_							)						->					Tuple:
          if strength < 0 or strength > 1:
              raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Any:
          # get the original timestep using init_timestep
          UpperCAmelCase									=					min(int(num_inference_steps * strength							)   ,       lowercase_							)

          UpperCAmelCase									=					max(num_inference_steps - init_timestep   ,       0							)
          UpperCAmelCase									=					self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=None							)						->					Optional[Any]:
          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_							)}"							)

          UpperCAmelCase									=					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."							)

          UpperCAmelCase									=					init_latents.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)

          # get latents
          print('add noise to latents at timestep'   ,       lowercase_							)
          UpperCAmelCase									=					self.scheduler.add_noise(lowercase_   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					init_latents

          return latents




      @torch.no_grad()
      def __call__(							self   ,       lowercase_ = None   ,       lowercase_ = 0.8   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = 0.0   ,       lowercase_ = 5_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          self.check_inputs(lowercase_							)

          # 2. Preprocess image
          UpperCAmelCase									=					preprocess(lowercase_							)

          # 3. set timesteps
          self.scheduler.set_timesteps(lowercase_   ,       device=self.device							)
          UpperCAmelCase			,							UpperCAmelCase									=					self.get_timesteps(lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					timesteps[:1].repeat(lowercase_							)

          # 4. Prepare latent variables
          UpperCAmelCase									=					self.prepare_latents(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.unet.dtype   ,       self.device   ,       lowercase_							)
          UpperCAmelCase									=					latents

          # 5. Denoising loop
          for t in self.progress_bar(lowercase_							):
              # 1. predict noise model_output
              UpperCAmelCase									=					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
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       eta=lowercase_   ,       use_clipped_model_output=lowercase_   ,       generator=lowercase_   ,       ).prev_sample

          UpperCAmelCase									=					(image / 2 + 0.5).clamp(0   ,       1							)
          UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).numpy()
          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

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

          return ImagePipelineOutput(images=lowercase_							)



