


'''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()

lowerCAmelCase      :List[Any]    	=			2
class _lowerCamelCase       :



		'''simple docstring'''







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







		def __eq__(					self		:						List[str]  ,     _A		:						Tuple			)  -> Any:
			return self.indices == other.indices







		def __getitem__(					self		:						Optional[int]  ,     _A		:						Optional[Any]			)  -> Union[str, Any]:
			if idx < len(self.symbols			):
				return self.symbols[idx]
			return self.unk_word







		def __len__(					self		:						Optional[Any]			)  -> Optional[int]:
			return len(self.symbols			)







		def __contains__(					self		:						Any  ,     _A		:						List[str]			)  -> List[Any]:
			return sym in self.indices







		@classmethod
		def    __lowerCAmelCase		(					cls		:						Any  ,     _A		:						Optional[Any]			)  -> Tuple:
			__magic_name__       :    Any     	=     cls()
			d.add_from_file(_A			)
			return d







		def    __lowerCAmelCase		(					self		:						Any  ,     _A		:						Optional[int]  ,     _A		:						List[Any]=1  ,     _A		:						Any=False			)  -> Dict:
			if word in self.indices and not overwrite:
				__magic_name__       :    Tuple     	=     self.indices[word]
				__magic_name__       :    Tuple     	=     self.count[idx] + n
				return idx
			else:
				__magic_name__       :    Union[str, Any]     	=     len(self.symbols			)
				__magic_name__       :    str     	=     idx
				self.symbols.append(_A			)
				self.count.append(_A			)
				return idx







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						str			)  -> Optional[int]:
			return 0







		def    __lowerCAmelCase		(					self		:						Optional[int]  ,     _A		:						List[Any]			)  -> Union[str, 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

			__magic_name__       :    Optional[Any]     	=     f.readlines()
			__magic_name__       :    Optional[Any]     	=     self._load_meta(_A			)

			for line in lines[indices_start_line:]:
				try:
					__magic_name__       :    Union[str, Any]     	=     line.rstrip().rsplit(' '  ,     1			)
					if field == "#fairseq:overwrite":
						__magic_name__       :    List[str]     	=     True
						__magic_name__       :    Any     	=     line.rsplit(' '  ,     1			)
					else:
						__magic_name__       :    Optional[Any]     	=     False
					__magic_name__       :    Optional[Any]     	=     int(_A			)
					__magic_name__       :    List[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 lowerCamelCase			(			lowerCAmelCase				:	Tuple		):





	"""simple docstring"""







	__magic_name__       :    Any     	=     dict((re.sub(R'@@$'		,		''		,		UpperCamelCase__		), v) if k.endswith('@@'		) else (re.sub(R'$'		,		'</w>'		,		UpperCamelCase__		), v) for k, v in d.items()		)
	__magic_name__       :    Optional[Any]     	=     '''<s> <pad> </s> <unk>'''.split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'{k}</w>']
		__magic_name__       :    Union[str, Any]     	=     d[k]  # restore
	return da





def lowerCamelCase			(			lowerCAmelCase				:	List[str]		,		lowerCAmelCase				:	List[str]		):





	"""simple docstring"""







	if not os.path.exists(UpperCamelCase__		):
		raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'		)
	os.makedirs(UpperCamelCase__		,		exist_ok=UpperCamelCase__		)
	print(f'Writing results to {pytorch_dump_folder_path}'		)

	# handle various types of models

	__magic_name__       :    List[Any]     	=     os.path.join(UpperCamelCase__		,		'checkpoint.pt'		)
	if not os.path.isfile(UpperCamelCase__		):
		raise ValueError(f'path to the file {checkpoint_file} does not exist!'		)
	__magic_name__       :    Optional[int]     	=     torch.load(UpperCamelCase__		,		map_location='cpu'		)

	__magic_name__       :    Optional[int]     	=     chkpt['''cfg''']['''model''']

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

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

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

	# model config
	__magic_name__       :    Dict     	=     os.path.join(UpperCamelCase__		,		'config.json'		)

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

	# tokenizer config
	__magic_name__       :    List[Any]     	=     os.path.join(UpperCamelCase__		,		UpperCamelCase__		)

	__magic_name__       :    List[Any]     	=     {
	    '''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(UpperCamelCase__		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(UpperCamelCase__		,		ensure_ascii=UpperCamelCase__		,		indent=UpperCamelCase__		)		)

	# model
	__magic_name__       :    Union[str, Any]     	=     chkpt['''model''']

	# remove unneeded keys
	__magic_name__       :    Optional[Any]     	=     [
	    '''decoder.version''',
	]
	for k in ignore_keys:
		model_state_dict.pop(UpperCamelCase__		,		UpperCamelCase__		)

	__magic_name__       :    Dict     	=     list(model_state_dict.keys()		)
	for layer_name in layer_names:
		if layer_name.endswith('output_projection.weight'		):
			__magic_name__       :    Dict     	=     model_state_dict.pop(UpperCamelCase__		)
		else:
			__magic_name__       :    Optional[Any]     	=     model_state_dict.pop(UpperCamelCase__		)

	__magic_name__       :    int     	=     BioGptConfig.from_pretrained(UpperCamelCase__		)
	__magic_name__       :    Union[str, Any]     	=     BioGptForCausalLM(UpperCamelCase__		)

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

	# save
	__magic_name__       :    Tuple     	=     os.path.join(UpperCamelCase__		,		UpperCamelCase__		)
	print(f'Generating {pytorch_weights_dump_path}'		)
	torch.save(UpperCamelCase__		,		UpperCamelCase__		)

	print('Conversion is done!'		)


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




"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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


