






import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort


class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Union[str, Any]   =       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							str=0			)		-> str:
		"""simple docstring"""







		__lowercase             =  np.random.RandomState(_lowerCAmelCase			)
		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """guidance_scale""": 7.5,
		    """output_type""": """numpy""",
		}
		return inputs







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  PNDMScheduler.from_config(pipe.scheduler.config       ,				skip_prk_steps=_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Tuple			)		-> int:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Union[str, Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs["""prompt"""]]

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs.pop("""prompt"""			)]

		__lowercase             =  pipe.tokenizer(
		    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
		__lowercase             =  text_inputs["""input_ids"""]

		__lowercase             =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

		__lowercase             =  prompt_embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4







	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * ["""this is a negative prompt"""]
		__lowercase             =  negative_prompt
		__lowercase             =  3 * [inputs["""prompt"""]]

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs.pop("""prompt"""			)]

		__lowercase             =  []
		for p in [prompt, negative_prompt]:
			__lowercase             =  pipe.tokenizer(
			    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
			__lowercase             =  text_inputs["""input_ids"""]

			embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]			)

		__lowercase       ,						__lowercase             =  embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4


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







	@property
	def  _a   (  self      :							Dict			)		-> str:
		"""simple docstring"""







		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)







	@property
	def  _a   (  self      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  ort.SessionOptions()
		__lowercase             =  False
		return options







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """CompVis/stable-diffusion-v1-4"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """A painting of a squirrel eating a burger"""
		np.random.seed(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=6.0       ,				num_inference_steps=10       ,				output_type="""np"""			)
		__lowercase             =  output.images

		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]			)

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







	def  _a   (  self      :							Tuple			)		-> Any:
		"""simple docstring"""







		__lowercase             =  DDIMScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]			)

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







	def  _a   (  self      :							Dict			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  LMSDiscreteScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]			)

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







	def  _a   (  self      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  0

		def test_callback_fn(_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							np.ndarray			) -> None:
			__lowercase             =  True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 0:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]			)

				assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3
			elif step == 5:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]			)

				assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3

		__lowercase             =  False

		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """Andromeda galaxy in a bottle"""

		__lowercase             =  np.random.RandomState(0			)
		pipe(
		    prompt=_lowerCAmelCase       ,				num_inference_steps=5       ,				guidance_scale=7.5       ,				generator=_lowerCAmelCase       ,				callback=_lowerCAmelCase       ,				callback_steps=1       ,				)
		assert test_callback_fn.has_been_called
		assert number_of_steps == 6







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		assert isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			)
		assert pipe.safety_checker is None

		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None

		# check that there's no error when saving a pipeline with one of the models being None
		with tempfile.TemporaryDirectory() as tmpdirname:
			pipe.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase			)

		# sanity check that the pipeline still works
		assert pipe.safety_checker is None
		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None









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      :      Tuple     						=						2


class        __UpperCamelCase    :







	def __init__(  self      :							List[str]       ,				*,  # begin keyword-only arguments
	    _lowerCAmelCase      :							Optional[int]="<s>"       ,				_lowerCAmelCase      :							Optional[int]="<pad>"       ,				_lowerCAmelCase      :							int="</s>"       ,				_lowerCAmelCase      :							str="<unk>"       ,				_lowerCAmelCase      :							List[str]=None       ,				)		-> Tuple:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  bos, unk, pad, eos
		__lowercase             =  []
		__lowercase             =  []
		__lowercase             =  {}
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  self.add_symbol(_lowerCAmelCase			)
		if extra_special_symbols:
			for s in extra_special_symbols:
				self.add_symbol(_lowerCAmelCase			)
		__lowercase             =  len(self.symbols			)







	def __eq__(  self      :							Dict       ,				_lowerCAmelCase      :							List[str]			)		-> Any:
		"""simple docstring"""







		return self.indices == other.indices







	def __getitem__(  self      :							Any       ,				_lowerCAmelCase      :							str			)		-> Dict:
		"""simple docstring"""







		if idx < len(self.symbols			):
			return self.symbols[idx]
		return self.unk_word







	def __len__(  self      :							str			)		-> List[str]:
		"""simple docstring"""







		return len(self.symbols			)







	def __contains__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		return sym in self.indices







	@classmethod
	def  _a   (  cls      :							Dict       ,				_lowerCAmelCase      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  cls()
		d.add_from_file(_lowerCAmelCase			)
		return d







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Union[str, Any]=1       ,				_lowerCAmelCase      :							Optional[int]=False			)		-> Union[str, Any]:
		"""simple docstring"""







		if word in self.indices and not overwrite:
			__lowercase             =  self.indices[word]
			__lowercase             =  self.count[idx] + n
			return idx
		else:
			__lowercase             =  len(self.symbols			)
			__lowercase             =  idx
			self.symbols.append(_lowerCAmelCase			)
			self.count.append(_lowerCAmelCase			)
			return idx







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[Any]:
		"""simple docstring"""







		return 0







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Dict			)		-> str:
		"""simple docstring"""







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

		__lowercase             =  f.readlines()
		__lowercase             =  self._load_meta(_lowerCAmelCase			)

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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''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

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

	__lowercase             =  chkpt["""cfg"""]["""model"""]

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

	__lowercase             =  os.path.join(lowerCamelCase				,  VOCAB_FILES_NAMES["""merges_file"""]					)
	shutil.copyfile(lowerCamelCase				,  lowerCamelCase					)

	# model config
	__lowercase             =  os.path.join(lowerCamelCase				,  """config.json"""					)

	__lowercase             =  {
	    """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
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)

	__lowercase             =  {
	    """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
	__lowercase             =  chkpt["""model"""]

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

	__lowercase             =  list(model_state_dict.keys()					)
	for layer_name in layer_names:
		if layer_name.endswith("""output_projection.weight"""					):
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)
		else:
			__lowercase             =  model_state_dict.pop(lowerCamelCase					)

	__lowercase             =  BioGptConfig.from_pretrained(lowerCamelCase					)
	__lowercase             =  BioGptForCausalLM(lowerCamelCase					)

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

	# save
	__lowercase             =  os.path.join(lowerCamelCase				,  lowerCamelCase					)
	print(F'Generating {pytorch_weights_dump_path}'					)
	torch.save(lowerCamelCase				,  lowerCamelCase					)

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


if __name__ == "__main__":
						__UpperCamelCase      :      int     						=						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)

