






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 				snake_case  (     lowerCamelCase=None				,  lowerCamelCase=None					):




	'''simple docstring'''







	return field(default_factory=lambda: default				,  metadata=lowerCamelCase					)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(
	    metadata={'help': 'The csv file to plot.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Disable logarithmic scale when plotting'}   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={
	        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
	    }   ,			)
	__snake_case	:Optional[str]   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}   ,			)
	__snake_case	:Optional[List[str]]   =       list_field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}   )





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	try:
		int(lowerCamelCase					)
		return True
	except ValueError:
		return False





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	try:
		float(lowerCamelCase					)
		return True
	except ValueError:
		return False


class        __UpperCamelCase    :







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







		__lowercase             =  args
		__lowercase             =  defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

		with open(self.args.csv_file       ,				newline=""""""			) as csv_file:
			__lowercase             =  csv.DictReader(_lowerCAmelCase			)
			for row in reader:
				__lowercase             =  row["""model"""]
				self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]			)			)
				self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]			)			)
				if can_convert_to_int(row["""result"""]			):
					# value is not None
					__lowercase             =  int(row["""result"""]			)
				elif can_convert_to_float(row["""result"""]			):
					# value is not None
					__lowercase             =  float(row["""result"""]			)







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







		__lowercase       ,						__lowercase             =  plt.subplots()
		__lowercase             =  """Time usage""" if self.args.is_time else """Memory usage"""
		__lowercase             =  title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

		if not self.args.no_log_scale:
			# set logarithm scales
			ax.set_xscale("""log"""			)
			ax.set_yscale("""log"""			)

		for axis in [ax.xaxis, ax.yaxis]:
			axis.set_major_formatter(ScalarFormatter()			)

		for model_name_idx, model_name in enumerate(self.result_dict.keys()			):
			__lowercase             =  sorted(set(self.result_dict[model_name]["""bsz"""]			)			)
			__lowercase             =  sorted(set(self.result_dict[model_name]["""seq_len"""]			)			)
			__lowercase             =  self.result_dict[model_name]["""result"""]

			((__lowercase)       ,						(__lowercase))             =  (
			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
			)

			__lowercase             =  (
			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
			)

			for inner_loop_value in inner_loop_array:
				if self.args.plot_along_batch:
					__lowercase             =  np.asarray(
					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,				dtype=_lowerCAmelCase       ,				)
				else:
					__lowercase             =  np.asarray(
					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,				dtype=np.floataa       ,				)

				((__lowercase)       ,						(__lowercase))             =  (
				    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
				)

				__lowercase             =  np.asarray(_lowerCAmelCase       ,				_lowerCAmelCase			)[: len(_lowerCAmelCase			)]
				plt.scatter(
				    _lowerCAmelCase       ,				_lowerCAmelCase       ,				label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'			)
				plt.plot(_lowerCAmelCase       ,				_lowerCAmelCase       ,				"""--"""			)

			title_str += F' {label_model_name} vs.'

		__lowercase             =  title_str[:-4]
		__lowercase             =  """Time in s""" if self.args.is_time else """Memory in MB"""

		# plot
		plt.title(_lowerCAmelCase			)
		plt.xlabel(_lowerCAmelCase			)
		plt.ylabel(_lowerCAmelCase			)
		plt.legend()

		if self.args.figure_png_file is not None:
			plt.savefig(self.args.figure_png_file			)
		else:
			plt.show()





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  HfArgumentParser(lowerCamelCase					)
	__lowercase             =  parser.parse_args_into_dataclasses()[0]
	__lowercase             =  Plot(args=lowerCamelCase					)
	plot.plot()


if __name__ == "__main__":
						main()









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)

