





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()

__UpperCAmelCase	:       Any    	=							2







class    __snake_case	:





       '''simple docstring'''







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




       def __eq__(  self			:   List[Any]							,    A			:   Dict		):
         return self.indices == other.indices




       def __getitem__(  self			:   Optional[Any]							,    A			:   List[str]		):
         if idx < len(self.symbols		):
           return self.symbols[idx]
         return self.unk_word




       def __len__(  self			:   Optional[int]		):
         return len(self.symbols		)




       def __contains__(  self			:   str							,    A			:   int		):
         return sym in self.indices




       @classmethod
       def 							UpperCAmelCase__			(  cls			:   int							,    A			:   Optional[Any]		):
         __snake_case:						int			  =						cls()
         d.add_from_file(A		)
         return d




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




       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   str		):
         return 0






       def 							UpperCAmelCase__			(  self			:   int							,    A			:   List[str]		):
         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

         __snake_case:						Dict			  =						f.readlines()
         __snake_case:						str			  =						self._load_meta(A		)

         for line in lines[indices_start_line:]:
           try:
             __snake_case  ,       __snake_case:						Optional[int]			  =						line.rstrip().rsplit(""" """							,    1		)
             if field == "#fairseq:overwrite":
               __snake_case:						List[str]			  =						True
               __snake_case  ,       __snake_case:						int			  =						line.rsplit(""" """							,    1		)
             else:
               __snake_case:						Tuple			  =						False
             __snake_case:						Any			  =						int(A		)
             __snake_case:						str			  =						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       A__							(   SCREAMING_SNAKE_CASE__)       ->   Tuple:
  # (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}
  __snake_case:						Union[str, Any]			  =						dict((re.sub(r"""@@$""" , """""" , SCREAMING_SNAKE_CASE__), v) if k.endswith("""@@""") else (re.sub(r"""$""" , """</w>""" , SCREAMING_SNAKE_CASE__), v) for k, v in d.items())
  __snake_case:						List[str]			  =						"""<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'''{k}</w>''']
    __snake_case:						Tuple			  =						d[k]  # restore
  return da







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

  # handle various types of models

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

  __snake_case:						Optional[int]			  =						chkpt["""cfg"""]["""model"""]

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

  # merges_file (bpecodes)
  __snake_case:						Any			  =						os.path.join(SCREAMING_SNAKE_CASE__ , """bpecodes""")
  if not os.path.isfile(SCREAMING_SNAKE_CASE__):
    raise ValueError(F'''path to the file {bpecodes_file} does not exist!''')

  __snake_case:						int			  =						os.path.join(SCREAMING_SNAKE_CASE__ , VOCAB_FILES_NAMES["""merges_file"""])
  shutil.copyfile(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  # model config
  __snake_case:						int			  =						os.path.join(SCREAMING_SNAKE_CASE__ , """config.json""")

  __snake_case:						Union[str, Any]			  =						{
      """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(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""") as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ , indent=SCREAMING_SNAKE_CASE__))

  # tokenizer config
  __snake_case:						Dict			  =						os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  __snake_case:						int			  =						{
      """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(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""") as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__ , ensure_ascii=SCREAMING_SNAKE_CASE__ , indent=SCREAMING_SNAKE_CASE__))

  # model
  __snake_case:						Any			  =						chkpt["""model"""]

  # remove unneeded keys
  __snake_case:						Optional[Any]			  =						[
      """decoder.version""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  __snake_case:						Optional[Any]			  =						list(model_state_dict.keys())
  for layer_name in layer_names:
    if layer_name.endswith("""output_projection.weight"""):
      __snake_case:						Dict			  =						model_state_dict.pop(SCREAMING_SNAKE_CASE__)
    else:
      __snake_case:						Dict			  =						model_state_dict.pop(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any			  =						BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
  __snake_case:						Tuple			  =						BioGptForCausalLM(SCREAMING_SNAKE_CASE__)

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

  # save
  __snake_case:						str			  =						os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  print(F'''Generating {pytorch_weights_dump_path}''')
  torch.save(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

  print("""Conversion is done!""")


if __name__ == "__main__":
      __UpperCAmelCase	:       List[str]    	=							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."
      )
      __UpperCAmelCase	:       Optional[Any]    	=							parser.parse_args()
      convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort







class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






