"""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        a_  (   ):





							'''simple docstring'''

							lowercase__    :	Union[str, Any]     					=			'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
							lowercase__    :	str     					=			Image.open(requests.get(_lowerCAmelCase     ,				stream=_lowerCAmelCase  ).raw  ).convert('RGB'  )

							return image





def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





							'''simple docstring'''

							lowercase__    :	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.layernorm.weight')  )
							rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')  )

							# fmt: on
							return rename_keys





def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				List[Any]  ):





							'''simple docstring'''

							lowercase__    :	Optional[int]     					=			dct.pop(_lowerCAmelCase  )
							lowercase__    :	Optional[int]     					=			val





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Dict  ):





							'''simple docstring'''

							for i in range(config.vision_config.num_hidden_layers  ):
														# read in original q and v biases
														lowercase__    :	Dict     					=			state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""  )
														lowercase__    :	Optional[int]     					=			state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""  )

														# next, set bias in the state dict
														lowercase__    :	List[Any]     					=			torch.cat((q_bias, torch.zeros_like(_lowerCAmelCase     ,				requires_grad=_lowerCAmelCase  ), v_bias)  )
														lowercase__    :	Optional[int]     					=			qkv_bias





def        a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Optional[Any]  ):





							'''simple docstring'''

							lowercase__    :	Optional[int]     					=			364 if 'coco' in model_name else 224
							lowercase__    :	str     					=			BlipaVisionConfig(image_size=_lowerCAmelCase  ).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:
														lowercase__    :	Union[str, Any]     					=			OPTConfig.from_pretrained('facebook/opt-2.7b'     ,				eos_token_id=_lowerCAmelCase  ).to_dict()
							elif "opt-6.7b" in model_name:
														lowercase__    :	Dict     					=			OPTConfig.from_pretrained('facebook/opt-6.7b'     ,				eos_token_id=_lowerCAmelCase  ).to_dict()
							elif "t5-xl" in model_name:
														lowercase__    :	Any     					=			TaConfig.from_pretrained('google/flan-t5-xl'     ,				dense_act_fn='gelu'     ,				bos_token_id=1  ).to_dict()
							elif "t5-xxl" in model_name:
														lowercase__    :	List[Any]     					=			TaConfig.from_pretrained('google/flan-t5-xxl'     ,				dense_act_fn='gelu'     ,				bos_token_id=1  ).to_dict()

							lowercase__    :	List[str]     					=			BlipaConfig(vision_config=_lowerCAmelCase     ,				text_config=_lowerCAmelCase  )

							return config, image_size





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Optional[Any]=None     ,				_lowerCAmelCase						:				List[Any]=False  ):





							'''simple docstring'''

							lowercase__    :	int     					=			(
							    AutoTokenizer.from_pretrained('facebook/opt-2.7b'  )
							    if 'opt' in model_name
							    else AutoTokenizer.from_pretrained('google/flan-t5-xl'  )
							)
							lowercase__    :	Any     					=			tokenizer('\n'     ,				add_special_tokens=_lowerCAmelCase  ).input_ids[0]
							lowercase__    :	Tuple     					=			get_blipa_config(_lowerCAmelCase     ,				eos_token_id=_lowerCAmelCase  )

							lowercase__    :	Optional[int]     					=			BlipaForConditionalGeneration(_lowerCAmelCase  ).eval()

							lowercase__    :	List[Any]     					=			{
							    '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'),
							}

							lowercase__    :	int     					=			model_name_to_original[model_name]

							# load original model
							print('Loading original model...'  )
							lowercase__    :	List[str]     					=			'cuda' if torch.cuda.is_available() else 'cpu'
							lowercase__    :	Dict     					=			load_model_and_preprocess(
							    name=_lowerCAmelCase     ,				model_type=_lowerCAmelCase     ,				is_eval=_lowerCAmelCase     ,				device=_lowerCAmelCase  )
							original_model.eval()
							print('Done!'  )

							# update state dict keys
							lowercase__    :	Optional[int]     					=			original_model.state_dict()
							lowercase__    :	int     					=			create_rename_keys(_lowerCAmelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														lowercase__    :	List[str]     					=			state_dict.pop(_lowerCAmelCase  )
														if key.startswith('Qformer.bert'  ):
																					lowercase__    :	Any     					=			key.replace('Qformer.bert'     ,				'qformer'  )
														if "attention.self" in key:
																					lowercase__    :	str     					=			key.replace('self'     ,				'attention'  )
														if "opt_proj" in key:
																					lowercase__    :	List[str]     					=			key.replace('opt_proj'     ,				'language_projection'  )
														if "t5_proj" in key:
																					lowercase__    :	str     					=			key.replace('t5_proj'     ,				'language_projection'  )
														if key.startswith('opt'  ):
																					lowercase__    :	Dict     					=			key.replace('opt'     ,				'language'  )
														if key.startswith('t5'  ):
																					lowercase__    :	Any     					=			key.replace('t5'     ,				'language'  )
														lowercase__    :	Optional[int]     					=			val

							# read in qv biases
							read_in_q_v_bias(_lowerCAmelCase     ,				_lowerCAmelCase  )

							lowercase__    :	List[Any]     					=			hf_model.load_state_dict(_lowerCAmelCase     ,				strict=_lowerCAmelCase  )
							assert len(_lowerCAmelCase  ) == 0
							assert unexpected_keys == ["qformer.embeddings.position_ids"]

							lowercase__    :	str     					=			load_demo_image()
							lowercase__    :	List[Any]     					=			vis_processors['eval'](_lowerCAmelCase  ).unsqueeze(0  ).to(_lowerCAmelCase  )
							lowercase__    :	List[str]     					=			tokenizer(['\n']     ,				return_tensors='pt'  ).input_ids.to(_lowerCAmelCase  )

							# create processor
							lowercase__    :	Any     					=			BlipImageProcessor(
							    size={'height': image_size, 'width': image_size}     ,				image_mean=_lowerCAmelCase     ,				image_std=_lowerCAmelCase  )
							lowercase__    :	Any     					=			BlipaProcessor(image_processor=_lowerCAmelCase     ,				tokenizer=_lowerCAmelCase  )
							lowercase__    :	Any     					=			processor(images=_lowerCAmelCase     ,				return_tensors='pt'  ).pixel_values.to(_lowerCAmelCase  )

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

							original_model.to(_lowerCAmelCase  )
							hf_model.to(_lowerCAmelCase  )
							with torch.no_grad():
														if "opt" in model_name:
																					lowercase__    :	List[str]     					=			original_model({'image': original_pixel_values, 'text_input': ['']}  ).logits
																					lowercase__    :	str     					=			hf_model(_lowerCAmelCase     ,				_lowerCAmelCase  ).logits
														else:
																					lowercase__    :	Dict     					=			original_model(
																					    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}  ).logits
																					lowercase__    :	Any     					=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id     ,				-100  )
																					lowercase__    :	Optional[int]     					=			hf_model(_lowerCAmelCase     ,				_lowerCAmelCase     ,				labels=_lowerCAmelCase  ).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":
														lowercase__    :	List[str]     					=			torch.tensor(
														    [[-41.5850, -4.4_4_4_0, -8.9_9_2_2], [-47.4322, -5.9_1_4_3, -1.7_3_4_0]]     ,				device=_lowerCAmelCase  )
														assert torch.allclose(logits[0, :3, :3]     ,				_lowerCAmelCase     ,				atol=1E-4  )
							elif model_name == "blip2-flan-t5-xl-coco":
														lowercase__    :	Optional[int]     					=			torch.tensor(
														    [[-57.0109, -9.8_9_6_7, -12.6280], [-68.6578, -12.7191, -10.5065]]     ,				device=_lowerCAmelCase  )
							else:
														# cast to same type
														lowercase__    :	Optional[Any]     					=			logits.dtype
														assert torch.allclose(original_logits.to(_lowerCAmelCase  )     ,				_lowerCAmelCase     ,				atol=1E-2  )
							print('Looks ok!'  )

							print('Generating a caption...'  )
							lowercase__    :	int     					=			''
							lowercase__    :	Dict     					=			tokenizer(_lowerCAmelCase     ,				return_tensors='pt'  ).input_ids.to(_lowerCAmelCase  )

							lowercase__    :	int     					=			original_model.generate({'image': original_pixel_values}  )
							lowercase__    :	Optional[int]     					=			hf_model.generate(
							    _lowerCAmelCase     ,				_lowerCAmelCase     ,				do_sample=_lowerCAmelCase     ,				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:'     ,				_lowerCAmelCase  )
							lowercase__    :	Optional[Any]     					=			input_ids.shape[1]
							lowercase__    :	Tuple     					=			processor.batch_decode(outputs[:, prompt_length:]     ,				skip_special_tokens=_lowerCAmelCase  )
							lowercase__    :	Union[str, Any]     					=			[text.strip() for text in output_text]
							print('HF generation:'     ,				_lowerCAmelCase  )

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

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


if __name__ == "__main__":
							_UpperCamelCase     :		Dict											=					argparse.ArgumentParser()
							_UpperCamelCase     :		Any											=					[
							    "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",
							)

							_UpperCamelCase     :		Tuple											=					parser.parse_args()

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


"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

