
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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
	lowercase	    =       Image.open(requests.get(__SCREAMING_SNAKE_CASE					,				stream=__SCREAMING_SNAKE_CASE				).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(__SCREAMING_SNAKE_CASE				).unsqueeze(0				).to(__SCREAMING_SNAKE_CASE				)
	return image






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

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

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

	return key






@torch.no_grad()
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None				):
	if config_path is not None:
		lowercase	    =       BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
	else:
		lowercase	    =       BlipConfig(projection_dim=512					,				text_config={}					,				vision_config={}				)

	lowercase	    =       BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE				).eval()

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

	lowercase	    =       blip_decoder(pretrained=__SCREAMING_SNAKE_CASE					,				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(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       rename_key(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       value

	hf_model.load_state_dict(__SCREAMING_SNAKE_CASE				)

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

	lowercase	    =       hf_model.generate(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

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

	lowercase	    =       hf_model.generate(__SCREAMING_SNAKE_CASE				)

	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(__SCREAMING_SNAKE_CASE				)

	# 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=__SCREAMING_SNAKE_CASE					,				image_size=__SCREAMING_SNAKE_CASE					,				vit='base'				)
	vqa_model.eval()

	lowercase	    =       vqa_model.state_dict()
	for key in modified_state_dict.copy():
		lowercase	    =       modified_state_dict.pop(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       rename_key(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       value

	lowercase	    =       BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE				)

	hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE				)

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

	lowercase	    =       hf_vqa_model.generate(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
	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=__SCREAMING_SNAKE_CASE					,				image_size=__SCREAMING_SNAKE_CASE					,				vit='base'				)
	itm_model.eval()

	lowercase	    =       itm_model.state_dict()
	for key in modified_state_dict.copy():
		lowercase	    =       modified_state_dict.pop(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       rename_key(__SCREAMING_SNAKE_CASE				)
		lowercase	    =       value

	lowercase	    =       BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE				)

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

	hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE				)
	hf_itm_model.eval()

	lowercase	    =       hf_itm_model(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				use_itm_head=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       hf_itm_model(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				use_itm_head=__SCREAMING_SNAKE_CASE				)

	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__":
	UpperCAmelCase       =					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''')
	UpperCAmelCase       =					parser.parse_args()

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



"""simple docstring"""
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				enum.Enum ):



							'''simple docstring'''





							_snake_case						      =		0
							_snake_case						      =		1
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''generated'''




							def __init__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Optional[int]:
													super().__init__(*snake_case_    ,				**snake_case_				)

													self.check_model_type(
													    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
													    if self.framework == """tf"""
													    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING				)




							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  {}
													if truncation is not None:
																			__lowerCAmelCase     =  truncation

													__lowerCAmelCase     =  generate_kwargs

													__lowerCAmelCase     =  {}
													if return_tensors is not None and return_type is None:
																			__lowerCAmelCase     =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
													if return_type is not None:
																			__lowerCAmelCase     =  return_type

													if clean_up_tokenization_spaces is not None:
																			__lowerCAmelCase     =  clean_up_tokenization_spaces

													if stop_sequence is not None:
																			__lowerCAmelCase     =  self.tokenizer.encode(snake_case_    ,				add_special_tokens=snake_case_				)
																			if len(snake_case_				) > 1:
																									warnings.warn(
																									    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
																									    """ the stop sequence will be used as the stop sequence string in the interim."""				)
																			__lowerCAmelCase     =  stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													return True




							def    A__						( self    ,				*snake_case_    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  self.model.config.prefix if self.model.config.prefix is not None else """"""
													if isinstance(args[0]    ,				snake_case_				):
																			if self.tokenizer.pad_token_id is None:
																									raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""				)
																			__lowerCAmelCase     =  ([prefix + arg for arg in args[0]],)
																			__lowerCAmelCase     =  True

													elif isinstance(args[0]    ,				snake_case_				):
																			__lowerCAmelCase     =  (prefix + args[0],)
																			__lowerCAmelCase     =  False
													else:
																			raise ValueError(
																			    f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""				)
													__lowerCAmelCase     =  self.tokenizer(*snake_case_    ,				padding=snake_case_    ,				truncation=snake_case_    ,				return_tensors=self.framework				)
													# This is produced by tokenizers but is an invalid generate kwargs
													if "token_type_ids" in inputs:
																			del inputs["token_type_ids"]
													return inputs




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  super().__call__(*snake_case_    ,				**snake_case_				)
													if (
													    isinstance(args[0]    ,				snake_case_				)
													    and all(isinstance(snake_case_    ,				snake_case_				) for el in args[0]				)
													    and all(len(snake_case_				) == 1 for res in result				)
													):
																			return [res[0] for res in result]
													return result




							def    A__						( self    ,				snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self._parse_and_tokenize(snake_case_    ,				truncation=snake_case_    ,				**snake_case_				)
													return inputs




							def    A__						( self    ,				snake_case_    ,				**snake_case_				)       ->  Union[str, Any]:
													if self.framework == "pt":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  model_inputs["""input_ids"""].shape
													elif self.framework == "tf":
																			__lowerCAmelCase   ,     __lowerCAmelCase     =  tf.shape(model_inputs["""input_ids"""]				).numpy()

													__lowerCAmelCase     =  generate_kwargs.get("""min_length"""    ,				self.model.config.min_length				)
													__lowerCAmelCase     =  generate_kwargs.get("""max_length"""    ,				self.model.config.max_length				)
													self.check_inputs(snake_case_    ,				generate_kwargs["""min_length"""]    ,				generate_kwargs["""max_length"""]				)
													__lowerCAmelCase     =  self.model.generate(**snake_case_    ,				**snake_case_				)
													__lowerCAmelCase     =  output_ids.shape[0]
													if self.framework == "pt":
																			__lowerCAmelCase     =  output_ids.reshape(snake_case_    ,				out_b // in_b    ,				*output_ids.shape[1:]				)
													elif self.framework == "tf":
																			__lowerCAmelCase     =  tf.reshape(snake_case_    ,				(in_b, out_b // in_b, *output_ids.shape[1:])				)
													return {"output_ids": output_ids}




							def    A__						( self    ,				snake_case_    ,				snake_case_=ReturnType.TEXT    ,				snake_case_=False				)       ->  Dict:
													__lowerCAmelCase     =  []
													for output_ids in model_outputs["output_ids"][0]:
																			if return_type == ReturnType.TENSORS:
																									__lowerCAmelCase     =  {f"""{self.return_name}_token_ids""": output_ids}
																			elif return_type == ReturnType.TEXT:
																									__lowerCAmelCase     =  {
																									    f"""{self.return_name}_text""": self.tokenizer.decode(
																									        snake_case_    ,				skip_special_tokens=snake_case_    ,				clean_up_tokenization_spaces=snake_case_    ,				)
																									}
																			records.append(snake_case_				)
													return records
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''summary'''




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  Tuple:
													return super().__call__(*snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  bool:
													if max_length < min_length:
																			logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""				)

													if input_length < max_length:
																			logger.warning(
																			    f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
																			    """a summarization task, where outputs shorter than the input are typically wanted, you might """
																			    f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""				)
@add_end_docstrings(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''translation'''




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Optional[int]:
													if input_length > 0.9 * max_length:
																			logger.warning(
																			    f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
																			    """increasing your max_length manually, e.g. translator('...', max_length=400)"""				)
													return True




							def    A__						( self    ,				*snake_case_    ,				snake_case_=TruncationStrategy.DO_NOT_TRUNCATE    ,				snake_case_=None    ,				snake_case_=None				)       ->  List[Any]:
													if getattr(self.tokenizer    ,				"""_build_translation_inputs"""    ,				snake_case_				):
																			return self.tokenizer._build_translation_inputs(
																			    *snake_case_    ,				return_tensors=self.framework    ,				truncation=snake_case_    ,				src_lang=snake_case_    ,				tgt_lang=snake_case_				)
													else:
																			return super()._parse_and_tokenize(*snake_case_    ,				truncation=snake_case_				)




							def    A__						( self    ,				snake_case_=None    ,				snake_case_=None    ,				**snake_case_				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  super()._sanitize_parameters(**snake_case_				)
													if src_lang is not None:
																			__lowerCAmelCase     =  src_lang
													if tgt_lang is not None:
																			__lowerCAmelCase     =  tgt_lang
													if src_lang is None and tgt_lang is None:
																			# Backward compatibility, direct arguments use is preferred.
																			__lowerCAmelCase     =  kwargs.get("""task"""    ,				self.task				)
																			__lowerCAmelCase     =  task.split("""_"""				)
																			if task and len(snake_case_				) == 4:
																									# translation, XX, to YY
																									__lowerCAmelCase     =  items[1]
																									__lowerCAmelCase     =  items[3]
													return preprocess_params, forward_params, postprocess_params




							def __call__( self    ,				*snake_case_    ,				**snake_case_				)       ->  List[Any]:
													return super().__call__(*snake_case_    ,				**snake_case_				)
