"""simple docstring"""





import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def  lowercase_ ( )			->   str:



							'''simple docstring'''

							__lowerCamelCase       :		List[str]		 =  "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
							__lowerCamelCase       :		int		 =  Image.open(requests.get(snake_case__    , stream=snake_case__   ).raw   ).convert("RGB"   )

							return image


def  lowercase_ ( _lowerCamelCase:						Dict   )			->   List[str]:



							'''simple docstring'''

							__lowerCamelCase       :		int		 =  []
							# fmt: off

							# vision encoder
							rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")   )
							rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")   )
							rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")   )
							rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")   )
							rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")   )
							rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")   )

							for i in range(config.vision_config.num_hidden_layers   ):
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")   )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")   )

							# QFormer
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")   )
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")   )

							# fmt: on
							return rename_keys


def  lowercase_ ( _lowerCamelCase:						Dict    , _lowerCamelCase:						int    , _lowerCamelCase:						str   )			->   Tuple:



							'''simple docstring'''

							__lowerCamelCase       :		Union[str, Any]		 =  dct.pop(snake_case__   )
							__lowerCamelCase       :		Any		 =  val


def  lowercase_ ( _lowerCamelCase:						Optional[int]    , _lowerCamelCase:						Union[str, Any]   )			->   Optional[Any]:



							'''simple docstring'''

							for i in range(config.vision_config.num_hidden_layers   ):
														# read in original q and v biases
														__lowerCamelCase       :		Union[str, Any]		 =  state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""   )
														__lowerCamelCase       :		List[str]		 =  state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""   )

														# next, set bias in the state dict
														__lowerCamelCase       :		Optional[int]		 =  torch.cat((q_bias, torch.zeros_like(snake_case__    , requires_grad=snake_case__   ), v_bias)   )
														__lowerCamelCase       :		Tuple		 =  qkv_bias


def  lowercase_ ( _lowerCamelCase:						Optional[int]    , _lowerCamelCase:						int   )			->   Tuple:



							'''simple docstring'''

							__lowerCamelCase       :		str		 =  364 if "coco" in model_name else 224
							__lowerCamelCase       :		int		 =  BlipaVisionConfig(image_size=snake_case__   ).to_dict()

							# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
							# seems like flan-T5 models don't have bos_token_id properly set?
							if "opt-2.7b" in model_name:
														__lowerCamelCase       :		int		 =  OPTConfig.from_pretrained("facebook/opt-2.7b"    , eos_token_id=snake_case__   ).to_dict()
							elif "opt-6.7b" in model_name:
														__lowerCamelCase       :		List[str]		 =  OPTConfig.from_pretrained("facebook/opt-6.7b"    , eos_token_id=snake_case__   ).to_dict()
							elif "t5-xl" in model_name:
														__lowerCamelCase       :		int		 =  TaConfig.from_pretrained("google/flan-t5-xl"    , dense_act_fn="gelu"    , bos_token_id=1   ).to_dict()
							elif "t5-xxl" in model_name:
														__lowerCamelCase       :		Any		 =  TaConfig.from_pretrained("google/flan-t5-xxl"    , dense_act_fn="gelu"    , bos_token_id=1   ).to_dict()

							__lowerCamelCase       :		Dict		 =  BlipaConfig(vision_config=snake_case__    , text_config=snake_case__   )

							return config, image_size


@torch.no_grad()
def  lowercase_ ( _lowerCamelCase:						Optional[Any]    , _lowerCamelCase:						List[Any]=None    , _lowerCamelCase:						List[Any]=False   )			->   str:



							'''simple docstring'''

							__lowerCamelCase       :		List[Any]		 =  (
							    AutoTokenizer.from_pretrained("facebook/opt-2.7b"   )
							    if "opt" in model_name
							    else AutoTokenizer.from_pretrained("google/flan-t5-xl"   )
							)
							__lowerCamelCase       :		str		 =  tokenizer("\n"    , add_special_tokens=snake_case__   ).input_ids[0]
							__lowerCamelCase ,	__lowerCamelCase       :		Optional[int]		 =  get_blipa_config(snake_case__    , eos_token_id=snake_case__   )

							__lowerCamelCase       :		List[Any]		 =  BlipaForConditionalGeneration(snake_case__   ).eval()

							__lowerCamelCase       :		str		 =  {
							    "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
							    "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
							    "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
							    "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
							    "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
							    "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
							    "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
							}

							__lowerCamelCase ,	__lowerCamelCase       :		List[str]		 =  model_name_to_original[model_name]

							# load original model
							print("Loading original model..."   )
							__lowerCamelCase       :		List[Any]		 =  "cuda" if torch.cuda.is_available() else "cpu"
							__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase       :		Any		 =  load_model_and_preprocess(
							    name=snake_case__    , model_type=snake_case__    , is_eval=snake_case__    , device=snake_case__   )
							original_model.eval()
							print("Done!"   )

							# update state dict keys
							__lowerCamelCase       :		Any		 =  original_model.state_dict()
							__lowerCamelCase       :		List[str]		 =  create_rename_keys(snake_case__   )
							for src, dest in rename_keys:
														rename_key(snake_case__    , snake_case__    , snake_case__   )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														__lowerCamelCase       :		int		 =  state_dict.pop(snake_case__   )
														if key.startswith("Qformer.bert"   ):
																					__lowerCamelCase       :		Any		 =  key.replace("Qformer.bert"    , "qformer"   )
														if "attention.self" in key:
																					__lowerCamelCase       :		Any		 =  key.replace("self"    , "attention"   )
														if "opt_proj" in key:
																					__lowerCamelCase       :		Any		 =  key.replace("opt_proj"    , "language_projection"   )
														if "t5_proj" in key:
																					__lowerCamelCase       :		Any		 =  key.replace("t5_proj"    , "language_projection"   )
														if key.startswith("opt"   ):
																					__lowerCamelCase       :		Any		 =  key.replace("opt"    , "language"   )
														if key.startswith("t5"   ):
																					__lowerCamelCase       :		str		 =  key.replace("t5"    , "language"   )
														__lowerCamelCase       :		int		 =  val

							# read in qv biases
							read_in_q_v_bias(snake_case__    , snake_case__   )

							__lowerCamelCase ,	__lowerCamelCase       :		int		 =  hf_model.load_state_dict(snake_case__    , strict=snake_case__   )
							assert len(snake_case__   ) == 0
							assert unexpected_keys == ["qformer.embeddings.position_ids"]

							__lowerCamelCase       :		Optional[Any]		 =  load_demo_image()
							__lowerCamelCase       :		int		 =  vis_processors["eval"](snake_case__   ).unsqueeze(0   ).to(snake_case__   )
							__lowerCamelCase       :		str		 =  tokenizer(["\n"]    , return_tensors="pt"   ).input_ids.to(snake_case__   )

							# create processor
							__lowerCamelCase       :		List[Any]		 =  BlipImageProcessor(
							    size={"height": image_size, "width": image_size}    , image_mean=snake_case__    , image_std=snake_case__   )
							__lowerCamelCase       :		str		 =  BlipaProcessor(image_processor=snake_case__    , tokenizer=snake_case__   )
							__lowerCamelCase       :		str		 =  processor(images=snake_case__    , return_tensors="pt"   ).pixel_values.to(snake_case__   )

							# make sure processor creates exact same pixel values
							assert torch.allclose(snake_case__    , snake_case__   )

							original_model.to(snake_case__   )
							hf_model.to(snake_case__   )
							with torch.no_grad():
														if "opt" in model_name:
																					__lowerCamelCase       :		Dict		 =  original_model({"image": original_pixel_values, "text_input": [""]}   ).logits
																					__lowerCamelCase       :		List[Any]		 =  hf_model(snake_case__    , snake_case__   ).logits
														else:
																					__lowerCamelCase       :		Tuple		 =  original_model(
																					    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}   ).logits
																					__lowerCamelCase       :		int		 =  input_ids.masked_fill(input_ids == tokenizer.pad_token_id    , -100   )
																					__lowerCamelCase       :		List[str]		 =  hf_model(snake_case__    , snake_case__    , labels=snake_case__   ).logits

							assert original_logits.shape == logits.shape
							print("First values of original logits:"    , original_logits[0, :3, :3]   )
							print("First values of HF logits:"    , logits[0, :3, :3]   )

							# assert values
							if model_name == "blip2-flan-t5-xl":
														__lowerCamelCase       :		int		 =  torch.tensor(
														    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]    , device=snake_case__   )
														assert torch.allclose(logits[0, :3, :3]    , snake_case__    , atol=1E-4   )
							elif model_name == "blip2-flan-t5-xl-coco":
														__lowerCamelCase       :		List[str]		 =  torch.tensor(
														    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]    , device=snake_case__   )
							else:
														# cast to same type
														__lowerCamelCase       :		Dict		 =  logits.dtype
														assert torch.allclose(original_logits.to(snake_case__   )    , snake_case__    , atol=1E-2   )
							print("Looks ok!"   )

							print("Generating a caption..."   )
							__lowerCamelCase       :		Dict		 =  ""
							__lowerCamelCase       :		List[str]		 =  tokenizer(snake_case__    , return_tensors="pt"   ).input_ids.to(snake_case__   )

							__lowerCamelCase       :		Union[str, Any]		 =  original_model.generate({"image": original_pixel_values}   )
							__lowerCamelCase       :		int		 =  hf_model.generate(
							    snake_case__    , snake_case__    , do_sample=snake_case__    , num_beams=5    , max_length=30    , min_length=1    , top_p=0.9    , repetition_penalty=1.0    , length_penalty=1.0    , temperature=1    , )
							print("Original generation:"    , snake_case__   )
							__lowerCamelCase       :		Tuple		 =  input_ids.shape[1]
							__lowerCamelCase       :		List[Any]		 =  processor.batch_decode(outputs[:, prompt_length:]    , skip_special_tokens=snake_case__   )
							__lowerCamelCase       :		str		 =  [text.strip() for text in output_text]
							print("HF generation:"    , snake_case__   )

							if pytorch_dump_folder_path is not None:
														processor.save_pretrained(snake_case__   )
														hf_model.save_pretrained(snake_case__   )

							if push_to_hub:
														processor.push_to_hub(F"""nielsr/{model_name}"""   )
														hf_model.push_to_hub(F"""nielsr/{model_name}"""   )


if __name__ == "__main__":
				__A	=					argparse.ArgumentParser()
				__A	=					[
				    '''blip2-opt-2.7b''',
				    '''blip2-opt-6.7b''',
				    '''blip2-opt-2.7b-coco''',
				    '''blip2-opt-6.7b-coco''',
				    '''blip2-flan-t5-xl''',
				    '''blip2-flan-t5-xl-coco''',
				    '''blip2-flan-t5-xxl''',
				]
				parser.add_argument(
				    '''--model_name''',
				    default='''blip2-opt-2.7b''',
				    choices=choices,
				    type=str,
				    help='''Path to hf config.json of model to convert''',
				)
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument(
				    '''--push_to_hub''',
				    action='''store_true''',
				    help='''Whether to push the model and processor to the hub after converting''',
				)

				__A	=					parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[str]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Union[str, Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Any   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   Union[str, Any]	,A_  :   str	,A_  :   str="<s>"	,A_  :   Any="</s>"	,A_  :   Tuple="</s>"	,A_  :   Any="<s>"	,A_  :   Optional[Any]="<unk>"	,A_  :   int="<pad>"	,A_  :   str="<mask>"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Optional[int]	,)  -> None:
							# Mask token behave like a normal word, i.e. include the space before it
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,unk_token=A_	,sep_token=A_	,cls_token=A_	,pad_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

							# for backward compatibility
							if not hasattr(self	,'sp_model_kwargs'   ):
								A     = {}

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A     = [self.cls_token_id]
							A     = [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is None:
								return [1] + ([0] * len(A_   )) + [1]
							return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]
							A     = [self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> List[str]:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)