






from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


_a      	=  logging.get_logger(__name__)  # pylint: disable=invalid-name

_a      	=  '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=8	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						_UpperCAmelCase									=			w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__()

							self.register_modules(
							    text_encoder=UpperCAmelCase       ,    tokenizer=UpperCAmelCase       ,    unet=UpperCAmelCase       ,    scheduler=UpperCAmelCase       ,    movq=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			2 ** (len(self.movq.config.block_out_channels    ) - 1)

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if latents is None:
													_UpperCAmelCase									=			randn_tensor(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    device=UpperCAmelCase       ,    dtype=UpperCAmelCase    )
							else:
													if latents.shape != shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
													_UpperCAmelCase									=			latents.to(UpperCAmelCase    )

							_UpperCAmelCase									=			latents * scheduler.init_noise_sigma
							return latents

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			len(UpperCAmelCase    ) if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) else 1
							# get prompt text embeddings
							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    padding='max_length'       ,    truncation=UpperCAmelCase       ,    max_length=77       ,    return_attention_mask=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors='pt'       ,    )

							_UpperCAmelCase									=			text_inputs.input_ids
							_UpperCAmelCase									=			self.tokenizer(UpperCAmelCase       ,    padding='longest'       ,    return_tensors='pt'    ).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
													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}"""    )

							_UpperCAmelCase									=			text_input_ids.to(UpperCAmelCase    )
							_UpperCAmelCase									=			text_inputs.attention_mask.to(UpperCAmelCase    )

							_UpperCAmelCase					, _UpperCAmelCase									=			self.text_encoder(
							    input_ids=UpperCAmelCase       ,    attention_mask=UpperCAmelCase    )

							_UpperCAmelCase									=			prompt_embeds.repeat_interleave(UpperCAmelCase       ,    dim=0    )
							_UpperCAmelCase									=			text_encoder_hidden_states.repeat_interleave(UpperCAmelCase       ,    dim=0    )
							_UpperCAmelCase									=			text_mask.repeat_interleave(UpperCAmelCase       ,    dim=0    )

							if do_classifier_free_guidance:
													_UpperCAmelCase									=			42
													if negative_prompt is None:
																			_UpperCAmelCase									=			[''] * batch_size
													elif type(UpperCAmelCase    ) is not type(UpperCAmelCase    ):
																			raise TypeError(
																			    F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase    )} !="""
																			    F""" {type(UpperCAmelCase    )}."""    )
													elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																			_UpperCAmelCase									=			[negative_prompt]
													elif batch_size != len(UpperCAmelCase    ):
																			raise ValueError(
																			    F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase    )}, but `prompt`:"""
																			    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																			    ' the batch size of `prompt`.'    )
													else:
																			_UpperCAmelCase									=			negative_prompt

													_UpperCAmelCase									=			self.tokenizer(
													    UpperCAmelCase       ,    padding='max_length'       ,    max_length=77       ,    truncation=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors='pt'       ,    )
													_UpperCAmelCase									=			uncond_input.input_ids.to(UpperCAmelCase    )
													_UpperCAmelCase									=			uncond_input.attention_mask.to(UpperCAmelCase    )

													_UpperCAmelCase					, _UpperCAmelCase									=			self.text_encoder(
													    input_ids=UpperCAmelCase       ,    attention_mask=UpperCAmelCase    )

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

													_UpperCAmelCase									=			negative_prompt_embeds.shape[1]
													_UpperCAmelCase									=			negative_prompt_embeds.repeat(1       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,    UpperCAmelCase    )

													_UpperCAmelCase									=			uncond_text_encoder_hidden_states.shape[1]
													_UpperCAmelCase									=			uncond_text_encoder_hidden_states.repeat(1       ,    UpperCAmelCase       ,    1    )
													_UpperCAmelCase									=			uncond_text_encoder_hidden_states.view(
													    batch_size * num_images_per_prompt       ,    UpperCAmelCase       ,    -1    )
													_UpperCAmelCase									=			uncond_text_mask.repeat_interleave(UpperCAmelCase       ,    dim=0    )

													# done duplicates

													# 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
													_UpperCAmelCase									=			torch.cat([negative_prompt_embeds, prompt_embeds]    )
													_UpperCAmelCase									=			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

													_UpperCAmelCase									=			torch.cat([uncond_text_mask, text_mask]    )

							return prompt_embeds, text_encoder_hidden_states, text_mask

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('Please install accelerate via `pip install accelerate`'    )

							_UpperCAmelCase									=			torch.device(F"""cuda:{gpu_id}"""    )

							_UpperCAmelCase									=			[
							    self.unet,
							    self.text_encoder,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							if is_accelerate_available() and is_accelerate_version('>='       ,    '0.17.0.dev0'    ):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

							_UpperCAmelCase									=			torch.device(F"""cuda:{gpu_id}"""    )

							if self.device.type != "cpu":
													self.to('cpu'       ,    silence_dtype_warnings=UpperCAmelCase    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							_UpperCAmelCase									=			None
							for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
													_UpperCAmelCase					, _UpperCAmelCase									=			cpu_offload_with_hook(UpperCAmelCase       ,    UpperCAmelCase       ,    prev_module_hook=UpperCAmelCase    )

							if self.safety_checker is not None:
													_UpperCAmelCase					, _UpperCAmelCase									=			cpu_offload_with_hook(self.safety_checker       ,    UpperCAmelCase       ,    prev_module_hook=UpperCAmelCase    )

							# We'll offload the last model manually.
							_UpperCAmelCase									=			hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if not hasattr(self.unet       ,    '_hf_hook'    ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(UpperCAmelCase       ,    '_hf_hook'    )
													    and hasattr(module._hf_hook       ,    'execution_device'    )
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device    )
							return self.device

	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase    )
	def __call__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 100       ,    UpperCAmelCase = 4.0       ,    UpperCAmelCase = 1       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "pil"       ,    UpperCAmelCase = True       ,    ):
							"""simple docstring"""







							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			1
							elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			len(UpperCAmelCase    )
							else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase    )}"""    )

							_UpperCAmelCase									=			self._execution_device

							_UpperCAmelCase									=			batch_size * num_images_per_prompt
							_UpperCAmelCase									=			guidance_scale > 1.0

							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self._encode_prompt(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			torch.cat(UpperCAmelCase       ,    dim=0    )
							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			torch.cat(UpperCAmelCase       ,    dim=0    )

							if do_classifier_free_guidance:
													_UpperCAmelCase									=			image_embeds.repeat_interleave(UpperCAmelCase       ,    dim=0    )
													_UpperCAmelCase									=			negative_image_embeds.repeat_interleave(UpperCAmelCase       ,    dim=0    )

							_UpperCAmelCase									=			torch.cat([negative_image_embeds, image_embeds]       ,    dim=0    ).to(
							    dtype=prompt_embeds.dtype       ,    device=UpperCAmelCase    )

							self.scheduler.set_timesteps(UpperCAmelCase       ,    device=UpperCAmelCase    )
							_UpperCAmelCase									=			self.scheduler.timesteps

							_UpperCAmelCase									=			self.unet.config.in_channels

							_UpperCAmelCase					, _UpperCAmelCase									=			get_new_h_w(UpperCAmelCase       ,    UpperCAmelCase       ,    self.movq_scale_factor    )

							# create initial latent
							_UpperCAmelCase									=			self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)       ,    text_encoder_hidden_states.dtype       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    self.scheduler       ,    )

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

													_UpperCAmelCase									=			{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
													_UpperCAmelCase									=			self.unet(
													    sample=UpperCAmelCase       ,    timestep=UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase       ,    added_cond_kwargs=UpperCAmelCase       ,    return_dict=UpperCAmelCase       ,    )[0]

													if do_classifier_free_guidance:
																			_UpperCAmelCase					, _UpperCAmelCase									=			noise_pred.split(latents.shape[1]       ,    dim=1    )
																			_UpperCAmelCase					, _UpperCAmelCase									=			noise_pred.chunk(2    )
																			_UpperCAmelCase					, _UpperCAmelCase									=			variance_pred.chunk(2    )
																			_UpperCAmelCase									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			_UpperCAmelCase									=			torch.cat([noise_pred, variance_pred_text]       ,    dim=1    )

													if not (
													    hasattr(self.scheduler.config       ,    'variance_type'    )
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																			_UpperCAmelCase					, _UpperCAmelCase									=			noise_pred.split(latents.shape[1]       ,    dim=1    )

													# compute the previous noisy sample x_t -> x_t-1
													_UpperCAmelCase									=			self.scheduler.step(
													    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    generator=UpperCAmelCase       ,    ).prev_sample
							# post-processing
							_UpperCAmelCase									=			self.movq.decode(UpperCAmelCase       ,    force_not_quantize=UpperCAmelCase    )['sample']

							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""    )

							if output_type in ["np", "pil"]:
													_UpperCAmelCase									=			image * 0.5 + 0.5
													_UpperCAmelCase									=			image.clamp(0       ,    1    )
													_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    ).float().numpy()

							if output_type == "pil":
													_UpperCAmelCase									=			self.numpy_to_pil(UpperCAmelCase    )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=UpperCAmelCase    )








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 __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    *,  # begin keyword-only arguments
	    UpperCAmelCase="<s>"       ,    UpperCAmelCase="<pad>"       ,    UpperCAmelCase="</s>"       ,    UpperCAmelCase="<unk>"       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			bos, unk, pad, eos
							_UpperCAmelCase									=			[]
							_UpperCAmelCase									=			[]
							_UpperCAmelCase									=			{}
							_UpperCAmelCase									=			self.add_symbol(UpperCAmelCase    )
							_UpperCAmelCase									=			self.add_symbol(UpperCAmelCase    )
							_UpperCAmelCase									=			self.add_symbol(UpperCAmelCase    )
							_UpperCAmelCase									=			self.add_symbol(UpperCAmelCase    )
							if extra_special_symbols:
													for s in extra_special_symbols:
																			self.add_symbol(UpperCAmelCase    )
							_UpperCAmelCase									=			len(self.symbols    )

	def __eq__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.indices == other.indices

	def __getitem__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if idx < len(self.symbols    ):
													return self.symbols[idx]
							return self.unk_word

	def __len__(							self    ):
							"""simple docstring"""







							return len(self.symbols    )

	def __contains__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return sym in self.indices

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			cls()
							d.add_from_file(UpperCAmelCase    )
							return d

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							if word in self.indices and not overwrite:
													_UpperCAmelCase									=			self.indices[word]
													_UpperCAmelCase									=			self.count[idx] + n
													return idx
							else:
													_UpperCAmelCase									=			len(self.symbols    )
													_UpperCAmelCase									=			idx
													self.symbols.append(UpperCAmelCase    )
													self.count.append(UpperCAmelCase    )
													return idx

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return 0

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							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

							_UpperCAmelCase									=			f.readlines()
							_UpperCAmelCase									=			self._load_meta(UpperCAmelCase    )

							for line in lines[indices_start_line:]:
													try:
																			_UpperCAmelCase					, _UpperCAmelCase									=			line.rstrip().rsplit(' '       ,    1    )
																			if field == "#fairseq:overwrite":
																									_UpperCAmelCase									=			True
																									_UpperCAmelCase					, _UpperCAmelCase									=			line.rsplit(' '       ,    1    )
																			else:
																									_UpperCAmelCase									=			False
																			_UpperCAmelCase									=			int(UpperCAmelCase    )
																			_UpperCAmelCase									=			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    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						_UpperCAmelCase									=			dict((re.sub(R'@@$' ,		'' ,		__lowerCAmelCase	), v) if k.endswith('@@'	) else (re.sub(R'$' ,		'</w>' ,		__lowerCAmelCase	), v) for k, v in d.items()	)
						_UpperCAmelCase									=			'<s> <pad> </s> <unk>'.split()
						# restore the special tokens
						for k in keep_keys:
												del da[F"""{k}</w>"""]
												_UpperCAmelCase									=			d[k]  # restore
						return da


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						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

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

						_UpperCAmelCase									=			chkpt['cfg']['model']

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

						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		VOCAB_FILES_NAMES['merges_file']	)
						shutil.copyfile(__lowerCAmelCase ,		__lowerCAmelCase	)

						# model config
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		'config.json'	)

						_UpperCAmelCase									=			{
						    '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
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		__lowerCAmelCase	)

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

						# model
						_UpperCAmelCase									=			chkpt['model']

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

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

						_UpperCAmelCase									=			BioGptConfig.from_pretrained(__lowerCAmelCase	)
						_UpperCAmelCase									=			BioGptForCausalLM(__lowerCAmelCase	)

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

						# save
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		__lowerCAmelCase	)
						print(F"""Generating {pytorch_weights_dump_path}"""	)
						torch.save(__lowerCAmelCase ,		__lowerCAmelCase	)

						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)
