




'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						lowerCamelCase__   (				a							):
						# vision encoder
						if "img_encoder.pos_embed" in name:
												__snake_case						 =   name.replace('img_encoder.pos_embed'    ,	'vision_model.embeddings.position_embeddings'							)
						if "img_encoder.patch_embed.proj" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.proj'    ,	'vision_model.embeddings.patch_embeddings.projection'							)
						if "img_encoder.patch_embed.norm" in name:
												__snake_case						 =   name.replace('img_encoder.patch_embed.norm'    ,	'vision_model.embeddings.layernorm'							)
						if "img_encoder.layers" in name:
												__snake_case						 =   name.replace('img_encoder.layers'    ,	'vision_model.encoder.stages'							)
						if "blocks" in name and "res" not in name:
												__snake_case						 =   name.replace('blocks'    ,	'layers'							)
						if "attn" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('attn'    ,	'self_attn'							)
						if "proj" in name and "self_attn" in name and "text" not in name:
												__snake_case						 =   name.replace('proj'    ,	'out_proj'							)
						if "pre_assign_attn.attn.proj" in name:
												__snake_case						 =   name.replace('pre_assign_attn.attn.proj'    ,	'pre_assign_attn.attn.out_proj'							)
						if "norm1" in name:
												__snake_case						 =   name.replace('norm1'    ,	'layer_norm1'							)
						if "norm2" in name and "pre_assign" not in name:
												__snake_case						 =   name.replace('norm2'    ,	'layer_norm2'							)
						if "img_encoder.norm" in name:
												__snake_case						 =   name.replace('img_encoder.norm'    ,	'vision_model.layernorm'							)
						# text encoder
						if "text_encoder.token_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.token_embedding'    ,	'text_model.embeddings.token_embedding'							)
						if "text_encoder.positional_embedding" in name:
												__snake_case						 =   name.replace('text_encoder.positional_embedding'    ,	'text_model.embeddings.position_embedding.weight'							)
						if "text_encoder.transformer.resblocks." in name:
												__snake_case						 =   name.replace('text_encoder.transformer.resblocks.'    ,	'text_model.encoder.layers.'							)
						if "ln_1" in name:
												__snake_case						 =   name.replace('ln_1'    ,	'layer_norm1'							)
						if "ln_2" in name:
												__snake_case						 =   name.replace('ln_2'    ,	'layer_norm2'							)
						if "c_fc" in name:
												__snake_case						 =   name.replace('c_fc'    ,	'fc1'							)
						if "c_proj" in name:
												__snake_case						 =   name.replace('c_proj'    ,	'fc2'							)
						if "text_encoder" in name:
												__snake_case						 =   name.replace('text_encoder'    ,	'text_model'							)
						if "ln_final" in name:
												__snake_case						 =   name.replace('ln_final'    ,	'final_layer_norm'							)
						# projection layers
						if "img_projector.linear_hidden." in name:
												__snake_case						 =   name.replace('img_projector.linear_hidden.'    ,	'visual_projection.'							)
						if "img_projector.linear_out." in name:
												__snake_case						 =   name.replace('img_projector.linear_out.'    ,	'visual_projection.3.'							)
						if "text_projector.linear_hidden" in name:
												__snake_case						 =   name.replace('text_projector.linear_hidden'    ,	'text_projection'							)
						if "text_projector.linear_out" in name:
												__snake_case						 =   name.replace('text_projector.linear_out'    ,	'text_projection.3'							)

						return name
def 						lowerCamelCase__   (				a    ,	a							):
						for key in orig_state_dict.copy().keys():
												__snake_case						 =   orig_state_dict.pop(a							)

												if "qkv" in key:
																		# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case ,       __snake_case						 =   int(key_split[2]							), int(key_split[4]							)
																		__snake_case						 =   config.vision_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[dim : dim * 2, :]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												elif "in_proj" in key:
																		# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																		# we need to split them up into separate matrices/vectors
																		__snake_case						 =   key.split('.'							)
																		__snake_case						 =   int(key_split[3]							)
																		__snake_case						 =   config.text_config.hidden_size
																		if "weight" in key:
																								__snake_case						 =   val[:dim, :]
																								__snake_case						 =   val[
																								    dim : dim * 2, :
																								]
																								__snake_case						 =   val[-dim:, :]
																		else:
																								__snake_case						 =   val[:dim]
																								__snake_case						 =   val[dim : dim * 2]
																								__snake_case						 =   val[-dim:]
												else:
																		__snake_case						 =   rename_key(a							)
																		# squeeze if necessary
																		if (
																		    "text_projection.0" in new_name
																		    or "text_projection.3" in new_name
																		    or "visual_projection.0" in new_name
																		    or "visual_projection.3" in new_name
																		):
																								__snake_case						 =   val.squeeze_()
																		else:
																								__snake_case						 =   val

						return orig_state_dict
def 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a="groupvit-gcc-yfcc"    ,	a=False							):
						__snake_case						 =   GroupViTConfig()
						__snake_case						 =   GroupViTModel(a							).eval()

						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)['model']
						__snake_case						 =   convert_state_dict(a    ,	a							)
						__snake_case ,       __snake_case						 =   model.load_state_dict(a    ,	strict=a							)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a							) == 0)

						# verify result
						__snake_case						 =   CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'							)
						__snake_case						 =   prepare_img()
						__snake_case						 =   processor(text=['a photo of a cat', 'a photo of a dog']    ,	images=a    ,	padding=a    ,	return_tensors='pt'							)

						with torch.no_grad():
												__snake_case						 =   model(**a							)

						if model_name == "groupvit-gcc-yfcc":
												__snake_case						 =   torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												__snake_case						 =   torch.tensor([[16.1_873, 8.6_230]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported.'							)
						assert torch.allclose(outputs.logits_per_image    ,	a    ,	atol=1E-3							)

						processor.save_pretrained(a							)
						model.save_pretrained(a							)
						print('Successfully saved processor and model to'    ,	a							)

						if push_to_hub:
												print('Pushing to the hub...'							)
												processor.push_to_hub(a    ,	organization='nielsr'							)
												model.push_to_hub(a    ,	organization='nielsr'							)


if __name__ == "__main__":
	_lowercase  =			argparse.ArgumentParser()
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
	)
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
	parser.add_argument(
	    """--model_name""",
	    default="""groupvit-gccy-fcc""",
	    type=str,
	    help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
	)
	_lowercase  =			parser.parse_args()

	convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''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)

