'''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								=   2


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				*,  # begin keyword-only arguments
       _a="<s>"       ,				_a="<pad>"       ,				_a="</s>"       ,				_a="<unk>"       ,				_a=None       ,				)    ->     Any:
        lowercase_				,lowercase_				,lowercase_				,lowercase_					:       Any     				=					bos, unk, pad, eos
        lowercase_					:       Optional[int]     				=					[]
        lowercase_					:       int     				=					[]
        lowercase_					:       Tuple     				=					{}
        lowercase_					:       str     				=					self.add_symbol(_a				)
        lowercase_					:       Any     				=					self.add_symbol(_a				)
        lowercase_					:       Dict     				=					self.add_symbol(_a				)
        lowercase_					:       List[str]     				=					self.add_symbol(_a				)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(_a				)
        lowercase_					:       Optional[Any]     				=					len(self.symbols				)
   def __eq__(self       ,				_a				)    ->     int:
        return self.indices == other.indices
   def __getitem__(self       ,				_a				)    ->     Tuple:
        if idx < len(self.symbols				):
             return self.symbols[idx]
        return self.unk_word
   def __len__(self				)    ->     Optional[Any]:
        return len(self.symbols				)
   def __contains__(self       ,				_a				)    ->     Union[str, Any]:
        return sym in self.indices
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a				)    ->     Tuple:
        lowercase_					:       str     				=					cls()
        d.add_from_file(_a				)
        return d
   def 		_lowerCamelCase (self       ,				_a       ,				_a=1       ,				_a=False				)    ->     Dict:
        if word in self.indices and not overwrite:
             lowercase_					:       List[Any]     				=					self.indices[word]
             lowercase_					:       Tuple     				=					self.count[idx] + n
             return idx
        else:
             lowercase_					:       Optional[Any]     				=					len(self.symbols				)
             lowercase_					:       int     				=					idx
             self.symbols.append(_a				)
             self.count.append(_a				)
             return idx
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        return 0





   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        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_					:       List[Any]     				=					f.readlines()
        lowercase_					:       Any     				=					self._load_meta(_a				)

        for line in lines[indices_start_line:]:
             try:
                  lowercase_				,lowercase_					:       int     				=					line.rstrip().rsplit(' '       ,				1				)
                  if field == "#fairseq:overwrite":
                       lowercase_					:       Optional[int]     				=					True
                       lowercase_				,lowercase_					:       str     				=					line.rsplit(' '       ,				1				)
                  else:
                       lowercase_					:       Dict     				=					False
                  lowercase_					:       Any     				=					int(_a				)
                  lowercase_					:       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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     # (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_					:       int     				=					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()							)
     lowercase_					:       Any     				=					'<s> <pad> </s> <unk>'.split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'''{k}</w>''']
          lowercase_					:       Optional[int]     				=					d[k]  # restore
     return da

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     # 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

     lowercase_					:       Dict     				=					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!'''							)
     lowercase_					:       int     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)

     lowercase_					:       Optional[Any]     				=					chkpt['cfg']['model']

     # dicts
     lowercase_					:       Optional[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!'''							)
     lowercase_					:       int     				=					Dictionary.load(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					rewrite_dict_keys(src_dict.indices							)
     lowercase_					:       Any     				=					len(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[Any]     				=					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)
     lowercase_					:       List[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!'''							)

     lowercase_					:       int     				=					os.path.join(SCREAMING_SNAKE_CASE_  ,       VOCAB_FILES_NAMES['merges_file']							)
     shutil.copyfile(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # model config
     lowercase_					:       Optional[Any]     				=					os.path.join(SCREAMING_SNAKE_CASE_  ,       'config.json'							)

     lowercase_					:       List[str]     				=					{
         '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
     lowercase_					:       List[str]     				=					os.path.join(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					{
         '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(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
     lowercase_					:       Tuple     				=					chkpt['model']

     # remove unneeded keys
     lowercase_					:       Union[str, Any]     				=					[
         'decoder.version',
     ]
     for k in ignore_keys:
          model_state_dict.pop(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

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

     lowercase_					:       Optional[Any]     				=					BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					BioGptForCausalLM(SCREAMING_SNAKE_CASE_							)

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

     # save
     lowercase_					:       Any     				=					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__":
 _A								=   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								=   parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					tempfile.mkdtemp()

        lowercase_					:       int     				=					[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        lowercase_					:       Union[str, Any]     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)

        lowercase_					:       Union[str, Any]     				=					{
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        lowercase_					:       List[str]     				=					os.path.join(self.tmpdirname       ,				_a				)
        with open(self.image_processor_file       ,				'w'       ,				encoding='utf-8'				) as fp:
             json.dump(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     int:
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        shutil.rmtree(self.tmpdirname				)
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       List[str]     				=					[np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta				)]
        lowercase_					:       Optional[Any]     				=					[Image.fromarray(np.moveaxis(_a       ,				0       ,				-1				)				) for x in image_inputs]
        return image_inputs
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       str     				=					self.get_tokenizer()
        lowercase_					:       Tuple     				=					self.get_rust_tokenizer()
        lowercase_					:       int     				=					self.get_image_processor()

        lowercase_					:       int     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_slow.save_pretrained(self.tmpdirname				)
        lowercase_					:       List[str]     				=					AlignProcessor.from_pretrained(self.tmpdirname       ,				use_fast=_a				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)
        processor_fast.save_pretrained(self.tmpdirname				)
        lowercase_					:       Any     				=					AlignProcessor.from_pretrained(self.tmpdirname				)

        self.assertEqual(processor_slow.tokenizer.get_vocab()       ,				tokenizer_slow.get_vocab()				)
        self.assertEqual(processor_fast.tokenizer.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertEqual(tokenizer_slow.get_vocab()       ,				tokenizer_fast.get_vocab()				)
        self.assertIsInstance(processor_slow.tokenizer       ,				_a				)
        self.assertIsInstance(processor_fast.tokenizer       ,				_a				)

        self.assertEqual(processor_slow.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertEqual(processor_fast.image_processor.to_json_string()       ,				image_processor.to_json_string()				)
        self.assertIsInstance(processor_slow.image_processor       ,				_a				)
        self.assertIsInstance(processor_fast.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       str     				=					AlignProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()				)
        processor.save_pretrained(self.tmpdirname				)

        lowercase_					:       Tuple     				=					self.get_tokenizer(bos_token='(BOS)'       ,				eos_token='(EOS)'				)
        lowercase_					:       str     				=					self.get_image_processor(do_normalize=_a       ,				padding_value=1.0				)

        lowercase_					:       Optional[Any]     				=					AlignProcessor.from_pretrained(
            self.tmpdirname       ,				bos_token='(BOS)'       ,				eos_token='(EOS)'       ,				do_normalize=_a       ,				padding_value=1.0				)

        self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab()				)
        self.assertIsInstance(processor.tokenizer       ,				_a				)

        self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()				)
        self.assertIsInstance(processor.image_processor       ,				_a				)
   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       Union[str, Any]     				=					self.get_image_processor()
        lowercase_					:       Union[str, Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[int]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					image_processor(_a       ,				return_tensors='np'				)
        lowercase_					:       Union[str, Any]     				=					processor(images=_a       ,				return_tensors='np'				)

        for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()       ,				input_processor[key].sum()       ,				delta=1e-2				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       List[Any]     				=					self.get_image_processor()
        lowercase_					:       int     				=					self.get_tokenizer()

        lowercase_					:       Any     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Tuple     				=					'lower newer'

        lowercase_					:       Optional[Any]     				=					processor(text=_a				)

        lowercase_					:       List[Any]     				=					tokenizer(_a       ,				padding='max_length'       ,				max_length=64				)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key]				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       Any     				=					self.get_image_processor()
        lowercase_					:       List[Any]     				=					self.get_tokenizer()

        lowercase_					:       List[str]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[Any]     				=					'lower newer'
        lowercase_					:       List[Any]     				=					self.prepare_image_inputs()

        lowercase_					:       List[str]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

        # test if it raises when no input is passed
        with pytest.raises(_a				):
             processor()
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Dict     				=					self.get_image_processor()
        lowercase_					:       Optional[Any]     				=					self.get_tokenizer()

        lowercase_					:       Optional[Any]     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Union[str, Any]     				=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_					:       str     				=					processor.batch_decode(_a				)
        lowercase_					:       Dict     				=					tokenizer.batch_decode(_a				)

        self.assertListEqual(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     List[str]:
        lowercase_					:       str     				=					self.get_image_processor()
        lowercase_					:       str     				=					self.get_tokenizer()

        lowercase_					:       str     				=					AlignProcessor(tokenizer=_a       ,				image_processor=_a				)

        lowercase_					:       Optional[int]     				=					'lower newer'
        lowercase_					:       Union[str, Any]     				=					self.prepare_image_inputs()

        lowercase_					:       Union[str, Any]     				=					processor(text=_a       ,				images=_a				)

        self.assertListEqual(list(inputs.keys()				)       ,				processor.model_input_names				)




