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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							'''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
					__magic_name__							:	List[str]    							=							Image.open(requests.get(__A							,stream=__A				).raw				).convert('''RGB'''				)

					return image







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							[]
					# 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.embeddings.layernorm.weight''')				)
					rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias''')				)

					# fmt: on
					return rename_keys







def 		lowercase__   (		__A:	str							,__A:	Union[str, Any]							,__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							dct.pop(__A				)
					__magic_name__							:	Union[str, Any]    							=							val







def 		lowercase__   (		__A:	str							,__A:	List[str]				):





					'''simple docstring'''






					for i in range(config.vision_config.num_hidden_layers				):
										# read in original q and v biases
										__magic_name__							:	Any    							=							state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''				)
										__magic_name__							:	str    							=							state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''				)

										# next, set bias in the state dict
										__magic_name__							:	int    							=							torch.cat((q_bias, torch.zeros_like(__A							,requires_grad=__A				), v_bias)				)
										__magic_name__							:	int    							=							qkv_bias







def 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	str    							=							3_6_4 if '''coco''' in model_name else 2_2_4
					__magic_name__							:	Optional[int]    							=							InstructBlipVisionConfig(image_size=__A				).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 "t5-xl" in model_name:
										__magic_name__							:	Tuple    							=							TaConfig.from_pretrained('''google/flan-t5-xl'''							,dense_act_fn='''gelu'''							,bos_token_id=1				).to_dict()
					elif "t5-xxl" in model_name:
										__magic_name__							:	Optional[int]    							=							TaConfig.from_pretrained('''google/flan-t5-xxl'''							,dense_act_fn='''gelu'''							,bos_token_id=1				).to_dict()
					elif "vicuna-7b" in model_name:
										__magic_name__							:	Tuple    							=							LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''							,vocab_size=3_2_0_0_1				).to_dict()
					elif "vicuna-13b" in model_name:
										__magic_name__							:	Optional[int]    							=							LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''							,vocab_size=3_2_0_0_1				).to_dict()
					else:
										raise ValueError('''Model name not supported'''				)

					# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
					__magic_name__							:	Optional[int]    							=							InstructBlipQFormerConfig(vocab_size=3_0_5_2_3				).to_dict()
					__magic_name__							:	int    							=							InstructBlipConfig(vision_config=__A							,text_config=__A							,qformer_config=__A				)

					return config, image_size







@torch.no_grad()
def 		lowercase__   (		__A:	List[str]							,__A:	List[Any]=None							,__A:	Tuple=False				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							AutoTokenizer.from_pretrained('''bert-base-uncased'''							,truncation_side='''left'''				)
					qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}				)

					if "t5" in model_name:
										__magic_name__							:	Optional[Any]    							=							TaTokenizerFast.from_pretrained('''google/flan-t5-xl'''							,truncation_side='''left'''				)
					elif "vicuna" in model_name:
										# the following was used in the original implementation:
										# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
										# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
										# tokenizer.add_special_tokens({"bos_token": "</s>"})
										# tokenizer.add_special_tokens({"eos_token": "</s>"})
										# tokenizer.add_special_tokens({"unk_token": "</s>"})
										__magic_name__							:	List[Any]    							=							LlamaTokenizerFast.from_pretrained(
										    '''huggyllama/llama-7b'''							,truncation_side='''left'''							,bos_token='''</s>'''							,unk_token='''</s>'''				)
										tokenizer.add_special_tokens({'''pad_token''': '''[PAD]'''}				)

					__magic_name__							:	Dict    							=							get_blipa_config(__A				)
					__magic_name__							:	Optional[Any]    							=							InstructBlipForConditionalGeneration(__A				).eval()

					__magic_name__							:	Optional[int]    							=							{
					    '''instructblip-vicuna-7b''': ('''blip2_vicuna_instruct''', '''vicuna7b'''),
					    '''instructblip-vicuna-13b''': ('''blip2_vicuna_instruct''', '''vicuna13b'''),
					    '''instructblip-flan-t5-xl''': ('''blip2_t5_instruct''', '''flant5xl'''),
					    '''instructblip-flan-t5-xxl''': ('''blip2_t5_instruct''', '''flant5xxl'''),
					}

					__magic_name__							:	Tuple    							=							model_name_to_original[model_name]

					# load original model
					print('''Loading original model...'''				)
					__magic_name__							:	List[Any]    							=							'''cuda:1''' if torch.cuda.is_available() else '''cpu'''
					__magic_name__							:	Tuple    							=							'''cuda:2''' if torch.cuda.is_available() else '''cpu'''
					__magic_name__							:	Optional[int]    							=							load_model_and_preprocess(
					    name=__A							,model_type=__A							,is_eval=__A							,device=__A				)
					original_model.eval()
					print('''Done!'''				)

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

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										__magic_name__							:	Any    							=							state_dict.pop(__A				)
										if key.startswith('''Qformer.bert'''				):
															__magic_name__							:	Any    							=							key.replace('''Qformer.bert'''							,'''qformer'''				)
										if "attention.self" in key:
															__magic_name__							:	Tuple    							=							key.replace('''self'''							,'''attention'''				)
										if "llm_proj" in key:
															__magic_name__							:	Dict    							=							key.replace('''llm_proj'''							,'''language_projection'''				)
										if "t5_proj" in key:
															__magic_name__							:	List[Any]    							=							key.replace('''t5_proj'''							,'''language_projection'''				)
										if key.startswith('''llm_model'''				):
															__magic_name__							:	Optional[int]    							=							key.replace('''llm_model'''							,'''language_model'''				)
										if key.startswith('''t5'''				):
															__magic_name__							:	List[Any]    							=							key.replace('''t5'''							,'''language'''				)
										__magic_name__							:	List[Any]    							=							val

					# read in qv biases
					read_in_q_v_bias(__A							,__A				)

					# note: weights get loaded in torch.float32 by default
					hf_model.load_state_dict(__A							,strict=__A				)

					__magic_name__							:	int    							=							load_demo_image()
					__magic_name__							:	Optional[int]    							=							'''What is unusual about this image?'''

					# create processor
					__magic_name__							:	List[str]    							=							BlipImageProcessor(
					    size={'''height''': image_size, '''width''': image_size}							,image_mean=__A							,image_std=__A				)
					__magic_name__							:	Dict    							=							InstructBlipProcessor(
					    image_processor=__A							,tokenizer=__A							,qformer_tokenizer=__A							,)
					__magic_name__							:	Dict    							=							processor(images=__A							,text=__A							,return_tensors='''pt'''				).to(__A				)

					# make sure processor creates exact same pixel values
					__magic_name__							:	List[Any]    							=							vis_processors['''eval'''](__A				).unsqueeze(0				).to(__A				)
					__magic_name__							:	str    							=							inputs.pixel_values
					assert torch.allclose(original_pixel_values.to(pixel_values.device				)							,__A				)

					original_model.to(__A				)
					hf_model.to(__A				)
					with torch.no_grad():
										if "vicuna" in model_name:
															__magic_name__							:	Union[str, Any]    							=							original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}				).logits
															__magic_name__							:	Tuple    							=							hf_model(**__A				).logits
										else:
															__magic_name__							:	Optional[Any]    							=							original_model(
															    {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}				).logits
															__magic_name__							:	Optional[Any]    							=							tokenizer('''\n'''							,return_tensors='''pt'''				).input_ids.to(__A				)
															__magic_name__							:	Union[str, Any]    							=							label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,-1_0_0				)
															__magic_name__							:	str    							=							hf_model(**__A							,labels=__A				).logits

					print('''First values of original logits:'''							,original_logits[0, :3, :3]				)
					print('''First values of HF logits:'''							,logits[0, :3, :3]				)

					# assert values
					assert original_logits.shape == logits.shape
					__magic_name__							:	List[Any]    							=							1e-4 if '''vicuna''' in model_name else 1e-5
					assert torch.allclose(original_logits.to(logits.device				)							,__A							,atol=__A				)
					print('''Looks ok!'''				)

					print('''Generating with original model...'''				)
					__magic_name__							:	str    							=							original_model.generate({'''image''': original_pixel_values, '''prompt''': prompt}							,num_beams=5				)

					# important: we need to cast the weights of the HF model to the appropriate type
					print('''Generating with HF model...'''				)
					__magic_name__							:	List[str]    							=							hf_model.generate(
					    **__A							,do_sample=__A							,num_beams=5							,max_length=2_5_6							,min_length=1							,top_p=0.9							,repetition_penalty=1.5							,length_penalty=1.0							,temperature=1							,)
					if "vicuna" in model_name:
										# convert output id 0 to 2 (eos_token_id)
										# TODO add this in the generate method?
										__magic_name__							:	Union[str, Any]    							=							2
					print('''Original generation:'''							,__A				)
					__magic_name__							:	Optional[Any]    							=							processor.batch_decode(__A							,skip_special_tokens=__A				)
					__magic_name__							:	Optional[int]    							=							[text.strip() for text in output_text]
					print('''HF generation:'''							,__A				)

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

					if push_to_hub:
										processor.push_to_hub(F'''Salesforce/{model_name}'''				)
										hf_model.push_to_hub(F'''Salesforce/{model_name}'''				)


if __name__ == "__main__":
	__lowerCamelCase   :						Union[str, Any]				=			argparse.ArgumentParser()
	__lowerCamelCase   :						str				=			[
	    '''instructblip-vicuna-7b''',
	    '''instructblip-vicuna-13b''',
	    '''instructblip-flan-t5-xl''',
	    '''instructblip-flan-t5-xxl''',
	]
	parser.add_argument(
	    '''--model_name''',
	    default='''instructblip-flan-t5-xl''',
	    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''',
	)

	__lowerCamelCase   :						List[Any]				=			parser.parse_args()

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









import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					return 1.0 / (1.0 + np.exp(-_outputs				))







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							np.max(_outputs							,axis=-1							,keepdims=__A				)
					__magic_name__							:	int    							=							np.exp(_outputs - maxes				)
					return shifted_exp / shifted_exp.sum(axis=-1							,keepdims=__A				)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''sigmoid'''
			UpperCamelCase__								='''softmax'''
			UpperCamelCase__								='''none'''






@add_end_docstrings(
    _lowerCamelCase				,R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''				,)
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=False
			UpperCamelCase__								=ClassificationFunction.NONE

			def __init__( self			:   Tuple       ,						**lowerCamelCase_			:   Tuple  )						->   List[Any]:
								super().__init__(**lowerCamelCase_  )

								self.check_model_type(
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
								    if self.framework == '''tf'''
								    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Tuple=None       ,						lowerCamelCase_			:   Dict=None       ,						lowerCamelCase_			:   Optional[int]=""       ,						**lowerCamelCase_			:   int  )						->   str:
								# Using "" as default argument because we're going to use `top_k=None` in user code to declare
								# "No top_k"
								__magic_name__							:	Dict    							=							tokenizer_kwargs

								__magic_name__							:	Dict    							=							{}
								if hasattr(self.model.config       ,						'''return_all_scores'''  ) and return_all_scores is None:
													__magic_name__							:	List[str]    							=							self.model.config.return_all_scores

								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or top_k is None:
													__magic_name__							:	Dict    							=							top_k
													__magic_name__							:	List[Any]    							=							False
								elif return_all_scores is not None:
													warnings.warn(
													    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
													    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''       ,						lowerCamelCase_       ,						)
													if return_all_scores:
																		__magic_name__							:	str    							=							None
													else:
																		__magic_name__							:	int    							=							1

								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													__magic_name__							:	str    							=							ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
													__magic_name__							:	List[str]    							=							function_to_apply
								return preprocess_params, {}, postprocess_params

			def __call__( self			:   Optional[Any]       ,						*lowerCamelCase_			:   Union[str, Any]       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Optional[int]    							=							super().__call__(*lowerCamelCase_       ,						**lowerCamelCase_  )
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__magic_name__							:	int    							=							'''top_k''' not in kwargs
								if isinstance(args[0]       ,						lowerCamelCase_  ) and _legacy:
													# This pipeline is odd, and return a list when single item is run
													return [result]
								else:
													return result

			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Any       ,						**lowerCamelCase_			:   int  )						->   Dict[str, GenericTensor]:
								__magic_name__							:	Union[str, Any]    							=							self.framework
								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													return self.tokenizer(**lowerCamelCase_       ,						return_tensors=lowerCamelCase_       ,						**lowerCamelCase_  )
								elif isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) and len(lowerCamelCase_  ) == 1 and isinstance(inputs[0]       ,						lowerCamelCase_  ) and len(inputs[0]  ) == 2:
													# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
													return self.tokenizer(
													    text=inputs[0][0]       ,						text_pair=inputs[0][1]       ,						return_tensors=lowerCamelCase_       ,						**lowerCamelCase_  )
								elif isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													# This is likely an invalid usage of the pipeline attempting to pass text pairs.
													raise ValueError(
													    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
													    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''  )
								return self.tokenizer(lowerCamelCase_       ,						return_tensors=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Dict  )						->   Union[str, Any]:
								return self.model(**lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Dict       ,						lowerCamelCase_			:   Dict=None       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Any=True  )						->   int:
								# `_legacy` is used to determine if we're running the naked pipeline and in backward
								# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
								# the more natural result containing the list.
								# Default value before `set_parameters`
								if function_to_apply is None:
													if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																		__magic_name__							:	Union[str, Any]    							=							ClassificationFunction.SIGMOID
													elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																		__magic_name__							:	str    							=							ClassificationFunction.SOFTMAX
													elif hasattr(self.model.config       ,						'''function_to_apply'''  ) and function_to_apply is None:
																		__magic_name__							:	Dict    							=							self.model.config.function_to_apply
													else:
																		__magic_name__							:	Optional[Any]    							=							ClassificationFunction.NONE

								__magic_name__							:	Any    							=							model_outputs['''logits'''][0]
								__magic_name__							:	Optional[int]    							=							outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
													__magic_name__							:	List[str]    							=							sigmoid(lowerCamelCase_  )
								elif function_to_apply == ClassificationFunction.SOFTMAX:
													__magic_name__							:	Optional[int]    							=							softmax(lowerCamelCase_  )
								elif function_to_apply == ClassificationFunction.NONE:
													__magic_name__							:	Optional[int]    							=							outputs
								else:
													raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''  )

								if top_k == 1 and _legacy:
													return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

								__magic_name__							:	Union[str, Any]    							=							[
								    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(lowerCamelCase_  )
								]
								if not _legacy:
													dict_scores.sort(key=lambda lowerCamelCase_  : x["score"]       ,						reverse=lowerCamelCase_  )
													if top_k is not None:
																		__magic_name__							:	Dict    							=							dict_scores[:top_k]
								return dict_scores



