

'''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()

__snake_case					:      int 		=     2



class lowerCamelCase					:



			'''simple docstring'''






			def __init__( self			:		Dict						,     *,  # begin keyword-only arguments
			    lowerCAmelCase_			:		Dict="<s>"						,     lowerCAmelCase_			:		str="<pad>"						,     lowerCAmelCase_			:		Optional[Any]="</s>"						,     lowerCAmelCase_			:		Dict="<unk>"						,     lowerCAmelCase_			:		Optional[int]=None						,     )     ->						Union[str, Any]:





									'''simple docstring'''
									A__       ,		A__       ,		A__       ,		A__	:  int           =bos, unk, pad, eos
									A__	:  Optional[int]           =[]
									A__	:  List[str]           =[]
									A__	:  Any           ={}
									A__	:  str           =self.add_symbol(__lowerCamelCase					)
									A__	:  Union[str, Any]           =self.add_symbol(__lowerCamelCase					)
									A__	:  Optional[int]           =self.add_symbol(__lowerCamelCase					)
									A__	:  int           =self.add_symbol(__lowerCamelCase					)
									if extra_special_symbols:
															for s in extra_special_symbols:
																					self.add_symbol(__lowerCamelCase					)
									A__	:  Union[str, Any]           =len(self.symbols					)


			def __eq__( self			:		Optional[int]						,     lowerCAmelCase_			:		List[str]					)     ->						Dict:





									'''simple docstring'''
									return self.indices == other.indices


			def __getitem__( self			:		Dict						,     lowerCAmelCase_			:		Tuple					)     ->						Dict:





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


			def __len__( self			:		Optional[Any]					)     ->						str:





									'''simple docstring'''
									return len(self.symbols					)


			def __contains__( self			:		Dict						,     lowerCAmelCase_			:		Tuple					)     ->						Optional[int]:





									'''simple docstring'''
									return sym in self.indices


			@classmethod
			def lowercase__		( cls			:		Union[str, Any]						,     lowerCAmelCase_			:		Union[str, Any]					)     ->						Optional[Any]:





									'''simple docstring'''
									A__	:  Tuple           =cls()
									d.add_from_file(__lowerCamelCase					)
									return d


			def lowercase__		( self			:		List[str]						,     lowerCAmelCase_			:		Optional[Any]						,     lowerCAmelCase_			:		List[str]=1						,     lowerCAmelCase_			:		Optional[int]=False					)     ->						Any:





									'''simple docstring'''
									if word in self.indices and not overwrite:
															A__	:  Any           =self.indices[word]
															A__	:  Optional[int]           =self.count[idx] + n
															return idx
									else:
															A__	:  Optional[int]           =len(self.symbols					)
															A__	:  Optional[Any]           =idx
															self.symbols.append(__lowerCamelCase					)
															self.count.append(__lowerCamelCase					)
															return idx


			def lowercase__		( self			:		Dict						,     lowerCAmelCase_			:		int					)     ->						List[Any]:





									'''simple docstring'''
									return 0



			def lowercase__		( self			:		str						,     lowerCAmelCase_			:		Optional[int]					)     ->						List[Any]:





									'''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

									A__	:  Dict           =f.readlines()
									A__	:  int           =self._load_meta(__lowerCamelCase					)

									for line in lines[indices_start_line:]:
															try:
																					A__       ,		A__	:  Dict           =line.rstrip().rsplit(""" """						,     1					)
																					if field == "#fairseq:overwrite":
																											A__	:  Optional[int]           =True
																											A__       ,		A__	:  Tuple           =line.rsplit(""" """						,     1					)
																					else:
																											A__	:  Tuple           =False
																					A__	:  Union[str, Any]           =int(__lowerCamelCase					)
																					A__	:  List[Any]           =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 		__lowerCamelCase			(  __snake_case	:						Union[str, Any]			)			->				List[str]:






						"""simple docstring"""





						A__	:  Dict           =dict((re.sub(r"""@@$""",						"""""",						lowerCAmelCase_			), v) if k.endswith("""@@"""			) else (re.sub(r"""$""",						"""</w>""",						lowerCAmelCase_			), v) for k, v in d.items()			)
						A__	:  int           ="""<s> <pad> </s> <unk>""".split()
						# restore the special tokens
						for k in keep_keys:
												del da[f"{k}</w>"]
												A__	:  str           =d[k]  # restore
						return da
def 		__lowerCamelCase			(  __snake_case	:						str,						__snake_case	:						Optional[int]			)			->				str:






						"""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

						A__	:  Union[str, Any]           =os.path.join(lowerCAmelCase_,						"""checkpoint.pt"""			)
						if not os.path.isfile(lowerCAmelCase_			):
												raise ValueError(f"path to the file {checkpoint_file} does not exist!"			)
						A__	:  Dict           =torch.load(lowerCAmelCase_,						map_location="""cpu"""			)

						A__	:  str           =chkpt["""cfg"""]["""model"""]

						# dicts
						A__	:  Tuple           =os.path.join(lowerCAmelCase_,						"""dict.txt"""			)
						if not os.path.isfile(lowerCAmelCase_			):
												raise ValueError(f"path to the file {dict_file} does not exist!"			)
						A__	:  Optional[Any]           =Dictionary.load(lowerCAmelCase_			)
						A__	:  Any           =rewrite_dict_keys(src_dict.indices			)
						A__	:  str           =len(lowerCAmelCase_			)
						A__	:  Optional[Any]           =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)
						A__	:  Any           =os.path.join(lowerCAmelCase_,						"""bpecodes"""			)
						if not os.path.isfile(lowerCAmelCase_			):
												raise ValueError(f"path to the file {bpecodes_file} does not exist!"			)

						A__	:  str           =os.path.join(lowerCAmelCase_,						VOCAB_FILES_NAMES["""merges_file"""]			)
						shutil.copyfile(lowerCAmelCase_,						lowerCAmelCase_			)

						# model config
						A__	:  Tuple           =os.path.join(lowerCAmelCase_,						"""config.json"""			)

						A__	:  int           ={
						    """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
						A__	:  Dict           =os.path.join(lowerCAmelCase_,						lowerCAmelCase_			)

						A__	:  Dict           ={
						    """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
						A__	:  Optional[int]           =chkpt["""model"""]

						# remove unneeded keys
						A__	:  List[Any]           =[
						    """decoder.version""",
						]
						for k in ignore_keys:
												model_state_dict.pop(lowerCAmelCase_,						lowerCAmelCase_			)

						A__	:  Union[str, Any]           =list(model_state_dict.keys()			)
						for layer_name in layer_names:
												if layer_name.endswith("""output_projection.weight"""			):
																		A__	:  int           =model_state_dict.pop(lowerCAmelCase_			)
												else:
																		A__	:  List[str]           =model_state_dict.pop(lowerCAmelCase_			)

						A__	:  Union[str, Any]           =BioGptConfig.from_pretrained(lowerCAmelCase_			)
						A__	:  str           =BioGptForCausalLM(lowerCAmelCase_			)

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

						# save
						A__	:  Any           =os.path.join(lowerCAmelCase_,						lowerCAmelCase_			)
						print(f"Generating {pytorch_weights_dump_path}"			)
						torch.save(lowerCAmelCase_,						lowerCAmelCase_			)

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


if __name__ == "__main__":
		__snake_case					:      Union[str, 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.'
		)
		__snake_case					:      int 		=     parser.parse_args()
		convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""






import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
     import jax.numpy as jnp

     from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):



   @slow
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      AutoTokenizer.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      '''The dog is cute and lives in the garden house'''
     _snake_case   	=      jnp.array([tokenizer.encode(__lowerCamelCase				)]				)

     _snake_case   	=      (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
     _snake_case   	=      jnp.array(
         [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]				)

     _snake_case   	=      model(__lowerCamelCase				)['''last_hidden_state''']
     self.assertEqual(output.shape							,       __lowerCamelCase				)
     # compare the actual values for a slice of last dim
     self.assertTrue(jnp.allclose(output[:, :, -1]							,       __lowerCamelCase							,       atol=1E-3				)				)





