






"""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		:       List[Any]  				=		2





class 					_UpperCAmelCase       :




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




					def __eq__(				self     :						str    ,						A     :						Tuple   )  ->  Any:
							return self.indices == other.indices




					def __getitem__(				self     :						int    ,						A     :						Tuple   )  ->  Any:
							if idx < len(self.symbols   ):
									return self.symbols[idx]
							return self.unk_word




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




					def __contains__(				self     :						Optional[Any]    ,						A     :						Optional[int]   )  ->  Dict:
							return sym in self.indices




					@classmethod
					def   A  (				cls     :						Optional[int]    ,						A     :						Dict   )  ->  Any:
							lowercase_ : Any							       =   cls()
							d.add_from_file(A   )
							return d




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




					def   A  (				self     :						int    ,						A     :						Tuple   )  ->  List[str]:
							return 0






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

							lowercase_ : Any							       =   f.readlines()
							lowercase_ : int							       =   self._load_meta(A   )

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



def 						lowercase    (				__snake_case				: Dict	):
		# (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}
		lowercase_ : Dict							       =   dict((re.sub(r'''@@$'''		,					''''''		,					__snake_case	), v) if k.endswith('''@@'''	) else (re.sub(r'''$'''		,					'''</w>'''		,					__snake_case	), v) for k, v in d.items()	)
		lowercase_ : int							       =   '''<s> <pad> </s> <unk>'''.split()
		# restore the special tokens
		for k in keep_keys:
				del da[F'''{k}</w>''']
				lowercase_ : Union[str, Any]							       =   d[k]  # restore
		return da



def 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Any	):
		# prep
		if not os.path.exists(__snake_case	):
				raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''	)
		os.makedirs(__snake_case		,					exist_ok=__snake_case	)
		print(F'''Writing results to {pytorch_dump_folder_path}'''	)

		# handle various types of models

		lowercase_ : Optional[Any]							       =   os.path.join(__snake_case		,					'''checkpoint.pt'''	)
		if not os.path.isfile(__snake_case	):
				raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''	)
		lowercase_ : Union[str, Any]							       =   torch.load(__snake_case		,					map_location='''cpu'''	)

		lowercase_ : int							       =   chkpt['''cfg''']['''model''']

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

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

		lowercase_ : List[Any]							       =   os.path.join(__snake_case		,					VOCAB_FILES_NAMES['''merges_file''']	)
		shutil.copyfile(__snake_case		,					__snake_case	)

		# model config
		lowercase_ : Union[str, Any]							       =   os.path.join(__snake_case		,					'''config.json'''	)

		lowercase_ : Dict							       =   {
		    '''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(__snake_case		,					'''w'''		,					encoding='''utf-8'''	) as f:
				f.write(json.dumps(__snake_case		,					ensure_ascii=__snake_case		,					indent=__snake_case	)	)

		# tokenizer config
		lowercase_ : Optional[int]							       =   os.path.join(__snake_case		,					__snake_case	)

		lowercase_ : str							       =   {
		    '''bos_token''': '''<s>''',
		    '''eos_token''': '''</s>''',
		    '''model_max_length''': 1_0_2_4,
		    '''pad_token''': '''<pad>''',
		    '''special_tokens_map_file''': None,
		    '''tokenizer_class''': '''BioGptTokenizer''',
		    '''unk_token''': '''<unk>''',
		}

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

		# model
		lowercase_ : Tuple							       =   chkpt['''model''']

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

		lowercase_ : List[Any]							       =   list(model_state_dict.keys()	)
		for layer_name in layer_names:
				if layer_name.endswith('''output_projection.weight'''	):
						lowercase_ : Optional[int]							       =   model_state_dict.pop(__snake_case	)
				else:
						lowercase_ : str							       =   model_state_dict.pop(__snake_case	)

		lowercase_ : int							       =   BioGptConfig.from_pretrained(__snake_case	)
		lowercase_ : int							       =   BioGptForCausalLM(__snake_case	)

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

		# save
		lowercase_ : Optional[int]							       =   os.path.join(__snake_case		,					__snake_case	)
		print(F'''Generating {pytorch_weights_dump_path}'''	)
		torch.save(__snake_case		,					__snake_case	)

		print('''Conversion is done!'''	)


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








'''simple docstring'''





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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def        a_     (							_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = image.size
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowerCamelCase       :       Optional[Any]           = image.resize((w, h)       ,resample=PIL_INTERPOLATION['lanczos']       )
  __lowerCamelCase       :       List[Any]           = np.array(_lowerCAmelCase       ).astype(np.floataa       ) / 255.0
  __lowerCamelCase       :       Optional[Any]           = image[None].transpose(0       ,3       ,1       ,2       )
  __lowerCamelCase       :       int           = torch.from_numpy(_lowerCAmelCase       )
  return 2.0 * image - 1.0






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	VQModel			,	_a      :	UNetaDModel			,	_a      :	Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]			,	)					->					Optional[Any]:
    super().__init__()
    self.register_modules(vqvae=_a			,	unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	List[Any]			,	_a      :	Union[torch.Tensor, PIL.Image.Image] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[int] = 100			,	_a      :	Optional[float] = 0.0			,	_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	)					->					Union[Tuple, ImagePipelineOutput]:
    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       Any           = 1
    elif isinstance(_a			,	torch.Tensor   ):
      __lowerCamelCase       :       Any           = image.shape[0]
    else:
      raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'   )

    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       List[str]           = preprocess(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = image.shape[-2:]

    # in_channels should be 6: 3 for latents, 3 for low resolution image
    __lowerCamelCase       :       Union[str, Any]           = (batch_size, self.unet.config.in_channels // 2, height, width)
    __lowerCamelCase       :       Tuple           = next(self.unet.parameters()   ).dtype

    __lowerCamelCase       :       Optional[int]           = randn_tensor(_a			,	generator=_a			,	device=self.device			,	dtype=_a   )

    __lowerCamelCase       :       Optional[int]           = image.to(device=self.device			,	dtype=_a   )

    # set timesteps and move to the correct device
    self.scheduler.set_timesteps(_a			,	device=self.device   )
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler.timesteps

    # 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       :       int           = 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
    __lowerCamelCase       :       List[str]           = {}
    if accepts_eta:
      __lowerCamelCase       :       str           = eta

    for t in self.progress_bar(_a   ):
      # concat latents and low resolution image in the channel dimension.
      __lowerCamelCase       :       str           = torch.cat([latents, image]			,	dim=1   )
      __lowerCamelCase       :       Union[str, Any]           = self.scheduler.scale_model_input(_a			,	_a   )
      # predict the noise residual
      __lowerCamelCase       :       Optional[int]           = self.unet(_a			,	_a   ).sample
      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a			,	**_a   ).prev_sample

    # decode the image latents with the VQVAE
    __lowerCamelCase       :       Union[str, Any]           = self.vqvae.decode(_a   ).sample
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_a			,	-1.0			,	1.0   )
    __lowerCamelCase       :       List[str]           = image / 2 + 0.5
    __lowerCamelCase       :       Optional[Any]           = image.cpu().permute(0			,	2			,	3			,	1   ).numpy()

    if output_type == "pil":
      __lowerCamelCase       :       Tuple           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






