




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor



class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def __init__(      self						:    Union[str, Any]      ,		a_						:    Union[str, Any]      ,		a_						:    List[str]=7      ,		a_						:    Optional[Any]=3      ,		a_						:    Any=18      ,		a_						:    Union[str, Any]=30      ,		a_						:    Any=400      ,		a_						:    int=True      ,		a_						:    Optional[int]=32      ,		a_						:    Dict=True      ,		):



						"""simple docstring"""




						__snake_case			=		parent
						__snake_case			=		batch_size
						__snake_case			=		num_channels
						__snake_case			=		image_size
						__snake_case			=		min_resolution
						__snake_case			=		max_resolution
						__snake_case			=		do_resize
						__snake_case			=		size_divisor
						__snake_case			=		do_rescale






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__  (			UpperCAmelCase__				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			GLPNImageProcessor if is_vision_available() else None





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		GLPNImageProcessingTester(self							)





			@property
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						return self.image_processor_tester.prepare_image_processor_dict()





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__lowerCAmelCase      ,		"do_resize"							)							)
						self.assertTrue(hasattr(__lowerCAmelCase      ,		"size_divisor"							)							)
						self.assertTrue(hasattr(__lowerCAmelCase      ,		"resample"							)							)
						self.assertTrue(hasattr(__lowerCAmelCase      ,		"do_rescale"							)							)





			def 							A (      self						:    Dict							):



						"""simple docstring"""




						pass





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase      ,		Image.Image							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)





			def 							A (      self						:    str							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCAmelCase      ,		numpify=__lowerCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase      ,		np.ndarray							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)






			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__snake_case			=		prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCAmelCase      ,		torchify=__lowerCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__lowerCAmelCase      ,		torch.Tensor							)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__snake_case			=		image_processing(image_inputs[0]      ,		return_tensors="pt"							).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0							)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0							)











'''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()

_lowercase  =			2
class 						a_				:




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




	def __eq__(       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[Any]      ):
							return self.indices == other.indices




	def __getitem__(       self     :  Optional[int]       ,		__lowerCAmelCase     :  Any      ):
							if idx < len(self.symbols      ):
													return self.symbols[idx]
							return self.unk_word




	def __len__(       self     :  Tuple      ):
							return len(self.symbols      )




	def __contains__(       self     :  str       ,		__lowerCAmelCase     :  Dict      ):
							return sym in self.indices




	@classmethod
	def   lowercase__   (       cls     :  str       ,		__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   cls()
							d.add_from_file(__lowerCAmelCase      )
							return d




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




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  int      ):
							return 0




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  List[str]      ):
							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

							__snake_case						 =   f.readlines()
							__snake_case						 =   self._load_meta(__lowerCAmelCase      )

							for line in lines[indices_start_line:]:
													try:
																			__snake_case ,       __snake_case						 =   line.rstrip().rsplit(' '       ,		1      )
																			if field == "#fairseq:overwrite":
																									__snake_case						 =   True
																									__snake_case ,       __snake_case						 =   line.rsplit(' '       ,		1      )
																			else:
																									__snake_case						 =   False
																			__snake_case						 =   int(__lowerCAmelCase      )
																			__snake_case						 =   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__   (				a							):
						# (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						 =   dict((re.sub(r'@@$'    ,	''    ,	a							), v) if k.endswith('@@'							) else (re.sub(r'$'    ,	'</w>'    ,	a							), v) for k, v in d.items()							)
						__snake_case						 =   '<s> <pad> </s> <unk>'.split()
						# restore the special tokens
						for k in keep_keys:
												del da[f'{k}</w>']
												__snake_case						 =   d[k]  # restore
						return da
def 						lowerCamelCase__   (				a    ,	a							):
						# prep
						if not os.path.exists(a							):
												raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'							)
						os.makedirs(a    ,	exist_ok=a							)
						print(f'Writing results to {pytorch_dump_folder_path}'							)

						# handle various types of models

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

						__snake_case						 =   chkpt['cfg']['model']

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

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

						__snake_case						 =   os.path.join(a    ,	VOCAB_FILES_NAMES['merges_file']							)
						shutil.copyfile(a    ,	a							)

						# model config
						__snake_case						 =   os.path.join(a    ,	'config.json'							)

						__snake_case						 =   {
						    '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-1_2,
						    '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(a    ,	'w'    ,	encoding='utf-8'							) as f:
												f.write(json.dumps(a    ,	ensure_ascii=a    ,	indent=a							)							)

						# tokenizer config
						__snake_case						 =   os.path.join(a    ,	a							)

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

						# model
						__snake_case						 =   chkpt['model']

						# remove unneeded keys
						__snake_case						 =   [
						    'decoder.version',
						]
						for k in ignore_keys:
												model_state_dict.pop(a    ,	a							)

						__snake_case						 =   list(model_state_dict.keys()							)
						for layer_name in layer_names:
												if layer_name.endswith('output_projection.weight'							):
																		__snake_case						 =   model_state_dict.pop(a							)
												else:
																		__snake_case						 =   model_state_dict.pop(a							)

						__snake_case						 =   BioGptConfig.from_pretrained(a							)
						__snake_case						 =   BioGptForCausalLM(a							)

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

						# save
						__snake_case						 =   os.path.join(a    ,	a							)
						print(f'Generating {pytorch_weights_dump_path}'							)
						torch.save(a    ,	a							)

						print('Conversion is done!'							)


if __name__ == "__main__":
	_lowercase  =			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."""
	)
	_lowercase  =			parser.parse_args()
	convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

