






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


A__			:    Tuple         =				logging.get_logger(__name__)


A__			:    Tuple         =				r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				@add_start_docstrings(lowerCamelCase						)
				def __call__(  self		:					Optional[Any],						lowerCamelCase		:					torch.LongTensor,						lowerCamelCase		:					torch.FloatTensor,						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								raise NotImplementedError('''StoppingCriteria needs to be subclassed'''						)



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Any,						lowerCamelCase		:					int,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								lowercase__     				=    max_length
								lowercase__     				=    max_position_embeddings




				@add_start_docstrings(lowerCamelCase						)
				def __call__(  self		:					Optional[Any],						lowerCamelCase		:					torch.LongTensor,						lowerCamelCase		:					torch.FloatTensor,						**lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    input_ids.shape[-1]
								lowercase__     				=    cur_len >= self.max_length
								if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
												logger.warning_once(
												    '''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
												    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
												    '''exceptions, performance degradation, or nothing at all.'''						)
								return is_done



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								warnings.warn(
								    '''The class `MaxNewTokensCriteria` is deprecated. '''
								    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
								    '''with `max_length = start_length + max_new_tokens` instead.''',						lowerCamelCase,						)
								lowercase__     				=    start_length
								lowercase__     				=    max_new_tokens
								lowercase__     				=    start_length + max_new_tokens




				@add_start_docstrings(lowerCamelCase						)
				def __call__(  self		:					Dict,						lowerCamelCase		:					torch.LongTensor,						lowerCamelCase		:					torch.FloatTensor,						**lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								return input_ids.shape[-1] >= self.max_length



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Dict,						lowerCamelCase		:					float,						lowerCamelCase		:					Optional[float] = None						):

								'''simple docstring'''
								lowercase__     				=    max_time
								lowercase__     				=    time.time() if initial_timestamp is None else initial_timestamp




				@add_start_docstrings(lowerCamelCase						)
				def __call__(  self		:					Optional[Any],						lowerCamelCase		:					torch.LongTensor,						lowerCamelCase		:					torch.FloatTensor,						**lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								return time.time() - self.initial_timestamp > self.max_time



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				@add_start_docstrings(lowerCamelCase						)
				def __call__(  self		:					Dict,						lowerCamelCase		:					torch.LongTensor,						lowerCamelCase		:					torch.FloatTensor,						**lowerCamelCase		:					str						):

								'''simple docstring'''
								return any(criteria(lowerCamelCase,						lowerCamelCase						) for criteria in self						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								for stopping_criterium in self:
												if isinstance(lowerCamelCase,						lowerCamelCase						):
																return stopping_criterium.max_length
												elif isinstance(lowerCamelCase,						lowerCamelCase						):
																return stopping_criterium.max_length
								return None






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    stopping_criteria.max_length
				lowercase__     				=    deepcopy(lowerCamelCase_       )
				if stopping_max_length is not None and stopping_max_length != max_length:
								warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter'''	,     lowerCamelCase_       )
				elif stopping_max_length is None:
								new_stopping_criteria.append(MaxLengthCriteria(max_length=lowerCamelCase_       )       )
				return new_stopping_criteria












import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
				lowercase__     				=    Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_       ).raw       ).convert('''RGB'''       )

				lowercase__     				=    transforms.Compose(
				    [
				        transforms.Resize((image_size, image_size)	,     interpolation=InterpolationMode.BICUBIC       ),
				        transforms.ToTensor(),
				        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)	,     (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)       ),
				    ]       )
				lowercase__     				=    transform(lowerCamelCase_       ).unsqueeze(0       ).to(lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if "visual_encoder" in key:
								lowercase__     				=    re.sub('''visual_encoder*'''	,     '''vision_model.encoder'''	,     lowerCamelCase_       )
				if "blocks" in key:
								lowercase__     				=    re.sub(r'''blocks'''	,     '''layers'''	,     lowerCamelCase_       )
				if "attn" in key:
								lowercase__     				=    re.sub(r'''attn'''	,     '''self_attn'''	,     lowerCamelCase_       )
				if "norm1" in key:
								lowercase__     				=    re.sub(r'''norm1'''	,     '''layer_norm1'''	,     lowerCamelCase_       )
				if "norm2" in key:
								lowercase__     				=    re.sub(r'''norm2'''	,     '''layer_norm2'''	,     lowerCamelCase_       )
				if "encoder.norm" in key:
								lowercase__     				=    re.sub(r'''encoder.norm'''	,     '''post_layernorm'''	,     lowerCamelCase_       )
				if "encoder.patch_embed.proj" in key:
								lowercase__     				=    re.sub(r'''encoder.patch_embed.proj'''	,     '''embeddings.patch_embedding'''	,     lowerCamelCase_       )

				if "encoder.pos_embed" in key:
								lowercase__     				=    re.sub(r'''encoder.pos_embed'''	,     '''embeddings.position_embedding'''	,     lowerCamelCase_       )
				if "encoder.cls_token" in key:
								lowercase__     				=    re.sub(r'''encoder.cls_token'''	,     '''embeddings.class_embedding'''	,     lowerCamelCase_       )

				if "self_attn" in key:
								lowercase__     				=    re.sub(r'''self_attn.proj'''	,     '''self_attn.projection'''	,     lowerCamelCase_       )

				return key






@torch.no_grad()
def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				if config_path is not None:
								lowercase__     				=    BlipConfig.from_pretrained(lowerCamelCase_       )
				else:
								lowercase__     				=    BlipConfig(projection_dim=512	,     text_config={}	,     vision_config={}       )

				lowercase__     				=    BlipForConditionalGeneration(lowerCamelCase_       ).eval()

				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

				lowercase__     				=    blip_decoder(pretrained=lowerCamelCase_	,     image_size=384	,     vit='''base'''       )
				lowercase__     				=    pt_model.eval()

				lowercase__     				=    pt_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				hf_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    384
				lowercase__     				=    load_demo_image(image_size=lowerCamelCase_	,     device='''cpu'''       )
				lowercase__     				=    BertTokenizer.from_pretrained('''bert-base-uncased'''       )
				lowercase__     				=    tokenizer(['''a picture of''']       ).input_ids

				lowercase__     				=    hf_model.generate(lowerCamelCase_	,     lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				lowercase__     				=    hf_model.generate(lowerCamelCase_       )

				assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

				if pytorch_dump_folder_path is not None:
								hf_model.save_pretrained(lowerCamelCase_       )

				# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
				lowercase__     				=    (
				    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
				)

				lowercase__     				=    blip_vqa(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				vqa_model.eval()

				lowercase__     				=    vqa_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForQuestionAnswering(lowerCamelCase_       )

				hf_vqa_model.load_state_dict(lowerCamelCase_       )

				lowercase__     				=    ['''How many dogs are in this image?''']
				lowercase__     				=    tokenizer(lowerCamelCase_	,     return_tensors='''pt'''       ).input_ids

				lowercase__     				=    hf_vqa_model.generate(lowerCamelCase_	,     lowerCamelCase_       )
				print(tokenizer.decode(answer[0]       )       )

				assert tokenizer.decode(answer[0]       ) == "[UNK] 1 [SEP]"
				if pytorch_dump_folder_path is not None:
								hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''       )

				lowercase__     				=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

				lowercase__     				=    blip_itm(pretrained=lowerCamelCase_	,     image_size=lowerCamelCase_	,     vit='''base'''       )
				itm_model.eval()

				lowercase__     				=    itm_model.state_dict()
				for key in modified_state_dict.copy():
								lowercase__     				=    modified_state_dict.pop(lowerCamelCase_       )
								lowercase__     				=    rename_key(lowerCamelCase_       )
								lowercase__     				=    value

				lowercase__     				=    BlipForImageTextRetrieval(lowerCamelCase_       )

				lowercase__     				=    ['''A picture of a woman with a dog sitting in a beach''']
				lowercase__     				=    tokenizer(
				    lowerCamelCase_	,     return_tensors='''pt'''	,     padding='''max_length'''	,     truncation=lowerCamelCase_	,     max_length=35	,     ).input_ids

				hf_itm_model.load_state_dict(lowerCamelCase_       )
				hf_itm_model.eval()

				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )
				lowercase__     				=    hf_itm_model(lowerCamelCase_	,     lowerCamelCase_	,     use_itm_head=lowerCamelCase_       )

				assert out[0].item() == 0.21_10_68_74_94_27_79_54
				assert torch.nn.functional.softmax(out_itm[0]	,     dim=1       )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

				if pytorch_dump_folder_path is not None:
								hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''       )


if __name__ == "__main__":
							A__			:    Optional[int]         =				argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							A__			:    List[Any]         =				parser.parse_args()

							convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




