
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:Optional[int]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,):
												super().__init__()
												self.register_modules(
												    vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,unet=A__   ,scheduler=A__   ,safety_checker=A__   ,feature_extractor=A__   ,)




							def  A__      (				self   ,A__ = "auto"):
												if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	lowercase							=						self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(A__)




							def  A__      (				self):
												self.enable_attention_slicing(A__)





							@torch.no_grad()
							def __call__(				self   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 5_0   ,A__ = 7.5   ,A__ = None   ,A__ = 1   ,A__ = 0.0   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,A__ = None   ,A__ = 1   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,A__):
																	lowercase							=						1
												elif isinstance(A__   ,A__):
																	lowercase							=						len(A__)
												else:
																	raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')

												if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(A__   ,A__) or callback_steps <= 0)
												):
																	raise ValueError(
																	    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    f' {type(A__)}.')

												# get prompt text embeddings
												lowercase							=						self.tokenizer(
												    A__   ,padding='''max_length'''   ,max_length=self.tokenizer.model_max_length   ,return_tensors='''pt'''   ,)
												lowercase							=						text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	lowercase							=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
																	logger.warning(
																	    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																	    f' {self.tokenizer.model_max_length} tokens: {removed_text}')
																	lowercase							=						text_input_ids[:, : self.tokenizer.model_max_length]

												if text_embeddings is None:
																	lowercase							=						self.text_encoder(text_input_ids.to(self.device))[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												lowercase	,						lowercase	,						lowercase							=						text_embeddings.shape
												lowercase							=						text_embeddings.repeat(1   ,A__   ,1)
												lowercase							=						text_embeddings.view(bs_embed * num_images_per_prompt   ,A__   ,-1)

												# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
												# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
												# corresponds to doing no classifier free guidance.
												lowercase							=						guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																	lowercase							=						42
																	if negative_prompt is None:
																						lowercase							=						['''''']
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__   ,A__):
																						lowercase							=						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    ''' the batch size of `prompt`.''')
																	else:
																						lowercase							=						negative_prompt

																	lowercase							=						text_input_ids.shape[-1]
																	lowercase							=						self.tokenizer(
																	    A__   ,padding='''max_length'''   ,max_length=A__   ,truncation=A__   ,return_tensors='''pt'''   ,)
																	lowercase							=						self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowercase							=						uncond_embeddings.shape[1]
																	lowercase							=						uncond_embeddings.repeat(A__   ,A__   ,1)
																	lowercase							=						uncond_embeddings.view(batch_size * num_images_per_prompt   ,A__   ,-1)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	lowercase							=						torch.cat([uncond_embeddings, text_embeddings])

												# get the initial random noise unless the user supplied it

												# Unlike in other pipelines, latents need to be generated in the target device
												# for 1-to-1 results reproducibility with the CompVis implementation.
												# However this currently doesn't work in `mps`.
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
												lowercase							=						text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(self.device)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device=self.device   ,dtype=A__)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device=self.device   ,dtype=A__)
												else:
																	if latents_reference.shape != latents_shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																	lowercase							=						latents_reference.to(self.device)
																	lowercase							=						latents.to(self.device)

												# This is the key part of the pipeline where we
												# try to ensure that the generated images w/ the same seed
												# but different sizes actually result in similar images
												lowercase							=						(latents_shape[3] - latents_shape_reference[3]) // 2
												lowercase							=						(latents_shape[2] - latents_shape_reference[2]) // 2
												lowercase							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
												lowercase							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
												lowercase							=						0 if dx < 0 else dx
												lowercase							=						0 if dy < 0 else dy
												lowercase							=						max(-dx   ,0)
												lowercase							=						max(-dy   ,0)
												# import pdb
												# pdb.set_trace()
												lowercase							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

												# set timesteps
												self.scheduler.set_timesteps(A__)

												# Some schedulers like PNDM have timesteps as arrays
												# It's more optimized to move all timesteps to correct device beforehand
												lowercase							=						self.scheduler.timesteps.to(self.device)

												# scale the initial noise by the standard deviation required by the scheduler
												lowercase							=						latents * self.scheduler.init_noise_sigma

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
												# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
												# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
												# and should be between [0, 1]
												lowercase							=						'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
												lowercase							=						{}
												if accepts_eta:
																	lowercase							=						eta

												for i, t in enumerate(self.progress_bar(A__)):
																	# expand the latents if we are doing classifier free guidance
																	lowercase							=						torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																	lowercase							=						self.scheduler.scale_model_input(A__   ,A__)

																	# predict the noise residual
																	lowercase							=						self.unet(A__   ,A__   ,encoder_hidden_states=A__).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																						lowercase	,						lowercase							=						noise_pred.chunk(2)
																						lowercase							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(A__   ,A__   ,A__   ,**A__).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																						callback(A__   ,A__   ,A__)

												lowercase							=						1 / 0.18215 * latents
												lowercase							=						self.vae.decode(A__).sample

												lowercase							=						(image / 2 + 0.5).clamp(0   ,1)

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if self.safety_checker is not None:
																	lowercase							=						self.feature_extractor(self.numpy_to_pil(A__)   ,return_tensors='''pt''').to(
																	    self.device)
																	lowercase	,						lowercase							=						self.safety_checker(
																	    images=A__   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
												else:
																	lowercase							=						None

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image, has_nsfw_concept)

												return StableDiffusionPipelineOutput(images=A__   ,nsfw_content_detected=A__)






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__		:str   =    2







class 				lowercase		:




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




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




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




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




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




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




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




							def  A__      (				self   ,A__):
												return 0





							def  A__      (				self   ,A__):
												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							=						f.readlines()
												lowercase							=						self._load_meta(A__)

												for line in lines[indices_start_line:]:
																	try:
																						lowercase	,						lowercase							=						line.rstrip().rsplit(''' '''   ,1)
																						if field == "#fairseq:overwrite":
																											lowercase							=						True
																											lowercase	,						lowercase							=						line.rsplit(''' '''   ,1)
																						else:
																											lowercase							=						False
																						lowercase							=						int(A__)
																						lowercase							=						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    (							lowerCAmelCase__		):
					'''simple docstring'''
					# (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							=						dict((re.sub(R'''@@$''' ,			'''''' ,			lowerCAmelCase__		), v) if k.endswith('''@@'''		) else (re.sub(R'''$''' ,			'''</w>''' ,			lowerCAmelCase__		), v) for k, v in d.items()		)
					lowercase							=						'''<s> <pad> </s> <unk>'''.split()
					# restore the special tokens
					for k in keep_keys:
										del da[f'{k}</w>']
										lowercase							=						d[k]  # restore
					return da



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# prep
					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

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

					lowercase							=						chkpt['''cfg''']['''model''']

					# dicts
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''dict.txt'''		)
					if not os.path.isfile(lowerCAmelCase__		):
										raise ValueError(f'path to the file {dict_file} does not exist!'		)
					lowercase							=						Dictionary.load(lowerCAmelCase__		)
					lowercase							=						rewrite_dict_keys(src_dict.indices		)
					lowercase							=						len(lowerCAmelCase__		)
					lowercase							=						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)
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''bpecodes'''		)
					if not os.path.isfile(lowerCAmelCase__		):
										raise ValueError(f'path to the file {bpecodes_file} does not exist!'		)

					lowercase							=						os.path.join(lowerCAmelCase__ ,			VOCAB_FILES_NAMES['''merges_file''']		)
					shutil.copyfile(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# model config
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''config.json'''		)

					lowercase							=						{
					    '''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
					lowercase							=						os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)

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

					# model
					lowercase							=						chkpt['''model''']

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

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

					lowercase							=						BioGptConfig.from_pretrained(lowerCAmelCase__		)
					lowercase							=						BioGptForCausalLM(lowerCAmelCase__		)

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

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

					print('''Conversion is done!'''		)


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




