
'''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 :




		def __init__(				self					,						*,  # begin keyword-only arguments
		    UpperCamelCase="<s>"					,						UpperCamelCase="<pad>"					,						UpperCamelCase="</s>"					,						UpperCamelCase="<unk>"					,						UpperCamelCase=None					,						):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= bos, unk, pad, eos
								_SCREAMING_SNAKE_CASE				= []
								_SCREAMING_SNAKE_CASE				= []
								_SCREAMING_SNAKE_CASE				= {}
								_SCREAMING_SNAKE_CASE				= self.add_symbol(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.add_symbol(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.add_symbol(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.add_symbol(UpperCamelCase      )
								if extra_special_symbols:
														for s in extra_special_symbols:
																				self.add_symbol(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= len(self.symbols      )




		def __eq__(				self					,						UpperCamelCase      ):
								return self.indices == other.indices




		def __getitem__(				self					,						UpperCamelCase      ):
								if idx < len(self.symbols      ):
														return self.symbols[idx]
								return self.unk_word




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




		def __contains__(				self					,						UpperCamelCase      ):
								return sym in self.indices




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= cls()
								d.add_from_file(UpperCamelCase      )
								return d




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=1					,						UpperCamelCase=False      ):
								if word in self.indices and not overwrite:
														_SCREAMING_SNAKE_CASE				= self.indices[word]
														_SCREAMING_SNAKE_CASE				= self.count[idx] + n
														return idx
								else:
														_SCREAMING_SNAKE_CASE				= len(self.symbols      )
														_SCREAMING_SNAKE_CASE				= idx
														self.symbols.append(UpperCamelCase      )
														self.count.append(UpperCamelCase      )
														return idx




		def     lowercase       (				self					,						UpperCamelCase      ):
								return 0




		def     lowercase       (				self					,						UpperCamelCase      ):
								if isinstance(UpperCamelCase					,						UpperCamelCase      ):
														try:
																				with open(UpperCamelCase					,						"r"					,						encoding="utf-8"      ) as fd:
																										self.add_from_file(UpperCamelCase      )
														except FileNotFoundError as fnfe:
																				raise fnfe
														except UnicodeError:
																				raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCamelCase      )      )
														return

								_SCREAMING_SNAKE_CASE				= f.readlines()
								_SCREAMING_SNAKE_CASE				= self._load_meta(UpperCamelCase      )

								for line in lines[indices_start_line:]:
														try:
																				_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= line.rstrip().rsplit(" "					,						1      )
																				if field == "#fairseq:overwrite":
																										_SCREAMING_SNAKE_CASE				= True
																										_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= line.rsplit(" "					,						1      )
																				else:
																										_SCREAMING_SNAKE_CASE				= False
																				_SCREAMING_SNAKE_CASE				= int(UpperCamelCase      )
																				_SCREAMING_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(UpperCamelCase      )      )
																				self.add_symbol(UpperCamelCase					,						n=UpperCamelCase					,						overwrite=UpperCamelCase      )
														except ValueError:
																				raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"      )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any  ):
						# (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}
						_SCREAMING_SNAKE_CASE				= 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()  )
						_SCREAMING_SNAKE_CASE				= "<s> <pad> </s> <unk>".split()
						# restore the special tokens
						for k in keep_keys:
												del da[F'{k}</w>']
												_SCREAMING_SNAKE_CASE				= d[k]  # restore
						return da



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   str  ):
						# 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

						_SCREAMING_SNAKE_CASE				= 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!'  )
						_SCREAMING_SNAKE_CASE				= torch.load(_SCREAMING_SNAKE_CASE	,			map_location="cpu"  )

						_SCREAMING_SNAKE_CASE				= chkpt["cfg"]["model"]

						# dicts
						_SCREAMING_SNAKE_CASE				= 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!'  )
						_SCREAMING_SNAKE_CASE				= Dictionary.load(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= rewrite_dict_keys(src_dict.indices  )
						_SCREAMING_SNAKE_CASE				= len(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= 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)
						_SCREAMING_SNAKE_CASE				= 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!'  )

						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			VOCAB_FILES_NAMES["merges_file"]  )
						shutil.copyfile(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						# model config
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			"config.json"  )

						_SCREAMING_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-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
						_SCREAMING_SNAKE_CASE				= os.path.join(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						_SCREAMING_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(_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
						_SCREAMING_SNAKE_CASE				= chkpt["model"]

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

						_SCREAMING_SNAKE_CASE				= list(model_state_dict.keys()  )
						for layer_name in layer_names:
												if layer_name.endswith("output_projection.weight"  ):
																		_SCREAMING_SNAKE_CASE				= model_state_dict.pop(_SCREAMING_SNAKE_CASE  )
												else:
																		_SCREAMING_SNAKE_CASE				= model_state_dict.pop(_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= BioGptForCausalLM(_SCREAMING_SNAKE_CASE  )

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

						# save
						_SCREAMING_SNAKE_CASE				= 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__":
						_snake_case				:   Optional[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."""
						)
						_snake_case				:   List[Any]     =				parser.parse_args()
						convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)

'''simple docstring'''







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 			lowerCAmelCase :




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=1					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=3					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(
								    sample_size=32					,						layers_per_block=[1, 2]					,						block_out_channels=[32, 64]					,						down_block_types=[
								        "ResnetDownsampleBlock2D",
								        "SimpleCrossAttnDownBlock2D",
								    ]					,						mid_block_type="UNetMidBlock2DSimpleCrossAttn"					,						up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]					,						in_channels=6					,						out_channels=6					,						cross_attention_dim=32					,						encoder_hid_dim=32					,						attention_head_dim=8					,						addition_embed_type="text"					,						addition_embed_type_num_heads=2					,						cross_attention_norm="group_norm"					,						resnet_time_scale_shift="scale_shift"					,						act_fn="gelu"					,						class_embed_type="timestep"					,						mid_block_scale_factor=1.4_14					,						time_embedding_act_fn="gelu"					,						time_embedding_dim=32					,						)
								unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						thresholding=UpperCamelCase					,						dynamic_thresholding_ratio=0.95					,						sample_max_value=1.0					,						prediction_type="epsilon"					,						variance_type="learned_range"					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= DDPMScheduler(
								    num_train_timesteps=1_000					,						beta_schedule="squaredcos_cap_v2"					,						beta_start=0.00_01					,						beta_end=0.02					,						)

								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["prompt"]
								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								if "image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "mask_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["mask_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if "original_image" in inputs:
														_SCREAMING_SNAKE_CASE				= inputs["original_image"]
								else:
														_SCREAMING_SNAKE_CASE				= None

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe.encode_prompt(UpperCamelCase      )

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
														setattr(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								for optional_component in pipe._optional_components:
														self.assertTrue(
														    getattr(UpperCamelCase					,						UpperCamelCase      ) is None					,						F'`{optional_component}` did not stay set to None after loading.'					,						)

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["generator"]
								_SCREAMING_SNAKE_CASE				= inputs["num_inference_steps"]
								_SCREAMING_SNAKE_CASE				= inputs["output_type"]

								# inputs with prompt converted to embeddings
								_SCREAMING_SNAKE_CASE				= {
								    "prompt_embeds": prompt_embeds,
								    "negative_prompt_embeds": negative_prompt_embeds,
								    "generator": generator,
								    "num_inference_steps": num_inference_steps,
								    "output_type": output_type,
								}

								if image is not None:
														_SCREAMING_SNAKE_CASE				= image

								if mask_image is not None:
														_SCREAMING_SNAKE_CASE				= mask_image

								if original_image is not None:
														_SCREAMING_SNAKE_CASE				= original_image

								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
														pipe.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.pipeline_class.from_pretrained(UpperCamelCase      )
														pipe_loaded.to(UpperCamelCase      )
														pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe_loaded(**UpperCamelCase      )[0]

								_SCREAMING_SNAKE_CASE				= np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
								self.assertLess(UpperCamelCase					,						1e-4      )