



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(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       ).convert("RGB"       )

					return image






def     a(      A       :    Tuple       )		->       List[str]:
					"""simple docstring"""





					a					     =   []
					# 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(      A       :    str ,     A       :    Tuple ,     A       :    Optional[int]       )		->       int:
					"""simple docstring"""





					a					     =   dct.pop(A       )
					a					     =   val






def     a(      A       :    Union[str, Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





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

										# next, set bias in the state dict
										a					     =   torch.cat((q_bias, torch.zeros_like(A ,     requires_grad=A       ), v_bias)       )
										a					     =   qkv_bias






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   364 if "coco" in model_name else 224
					a					     =   BlipaVisionConfig(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 "opt-2.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-2.7b" ,     eos_token_id=A       ).to_dict()
					elif "opt-6.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,     eos_token_id=A       ).to_dict()
					elif "t5-xl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()
					elif "t5-xxl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xxl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()

					a					     =   BlipaConfig(vision_config=A ,     text_config=A       )

					return config, image_size






@torch.no_grad()
def     a(      A       :    Optional[int] ,     A       :    List[str]=None ,     A       :    Optional[int]=False       )		->       Any:
					"""simple docstring"""





					a					     =   (
					    AutoTokenizer.from_pretrained("facebook/opt-2.7b"       )
					    if "opt" in model_name
					    else AutoTokenizer.from_pretrained("google/flan-t5-xl"       )
					)
					a					     =   tokenizer("\n" ,     add_special_tokens=A       ).input_ids[0]
					a     ,				a					     =   get_blipa_config(A ,     eos_token_id=A       )

					a					     =   BlipaForConditionalGeneration(A       ).eval()

					a					     =   {
					    "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"),
					}

					a     ,				a					     =   model_name_to_original[model_name]

					# load original model
					print("Loading original model..."       )
					a					     =   "cuda" if torch.cuda.is_available() else "cpu"
					a     ,				a     ,				a					     =   load_model_and_preprocess(
					    name=A ,     model_type=A ,     is_eval=A ,     device=A       )
					original_model.eval()
					print("Done!"       )

					# update state dict keys
					a					     =   original_model.state_dict()
					a					     =   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():
										a					     =   state_dict.pop(A       )
										if key.startswith("Qformer.bert"       ):
															a					     =   key.replace("Qformer.bert" ,     "qformer"       )
										if "attention.self" in key:
															a					     =   key.replace("self" ,     "attention"       )
										if "opt_proj" in key:
															a					     =   key.replace("opt_proj" ,     "language_projection"       )
										if "t5_proj" in key:
															a					     =   key.replace("t5_proj" ,     "language_projection"       )
										if key.startswith("opt"       ):
															a					     =   key.replace("opt" ,     "language"       )
										if key.startswith("t5"       ):
															a					     =   key.replace("t5" ,     "language"       )
										a					     =   val

					# read in qv biases
					read_in_q_v_bias(A ,     A       )

					a     ,				a					     =   hf_model.load_state_dict(A ,     strict=A       )
					assert len(A       ) == 0
					assert unexpected_keys == ["qformer.embeddings.position_ids"]

					a					     =   load_demo_image()
					a					     =   vis_processors["eval"](A       ).unsqueeze(0       ).to(A       )
					a					     =   tokenizer(["\n"] ,     return_tensors="pt"       ).input_ids.to(A       )

					# create processor
					a					     =   BlipImageProcessor(
					    size={"height": image_size, "width": image_size} ,     image_mean=A ,     image_std=A       )
					a					     =   BlipaProcessor(image_processor=A ,     tokenizer=A       )
					a					     =   processor(images=A ,     return_tensors="pt"       ).pixel_values.to(A       )

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

					original_model.to(A       )
					hf_model.to(A       )
					with torch.no_grad():
										if "opt" in model_name:
															a					     =   original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
															a					     =   hf_model(A ,     A       ).logits
										else:
															a					     =   original_model(
															    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}       ).logits
															a					     =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,     -100       )
															a					     =   hf_model(A ,     A ,     labels=A       ).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":
										a					     =   torch.tensor(
										    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] ,     device=A       )
										assert torch.allclose(logits[0, :3, :3] ,     A ,     atol=1e-4       )
					elif model_name == "blip2-flan-t5-xl-coco":
										a					     =   torch.tensor(
										    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] ,     device=A       )
					else:
										# cast to same type
										a					     =   logits.dtype
										assert torch.allclose(original_logits.to(A       ) ,     A ,     atol=1e-2       )
					print("Looks ok!"       )

					print("Generating a caption..."       )
					a					     =   ""
					a					     =   tokenizer(A ,     return_tensors="pt"       ).input_ids.to(A       )

					a					     =   original_model.generate({"image": original_pixel_values}       )
					a					     =   hf_model.generate(
					    A ,     A ,     do_sample=A ,     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:" ,     A       )
					a					     =   input_ids.shape[1]
					a					     =   processor.batch_decode(outputs[:, prompt_length:] ,     skip_special_tokens=A       )
					a					     =   [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'''nielsr/{model_name}'''       )
										hf_model.push_to_hub(f'''nielsr/{model_name}'''       )


if __name__ == "__main__":
				_lowercase: Dict      			=		argparse.ArgumentParser()
				_lowercase: Tuple      			=		[
				    "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",
				)

				_lowercase: Any      			=		parser.parse_args()

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





import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_lowercase: Optional[int]      			=		re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowercase: Dict      			=		10
_lowercase: Optional[Any]      			=		256






def     a(      A       :    List[str]       )		->       Optional[MinHash]:
					"""simple docstring"""





					if len(A       ) < MIN_NUM_TOKENS:
										return None
					a					     =   MinHash(num_perm=A       )
					for token in set(A       ):
										min_hash.update(token.encode()       )
					return min_hash






def     a(      A       :    str       )		->       Set[str]:
					"""simple docstring"""





					return {t for t in NON_ALPHA.split(A       ) if len(t.strip()       ) > 0}



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       *,
	    lowerCamelCase_ = 0.85		,       ):
						"""simple docstring"""

						a					     =   duplication_jaccard_threshold
						a					     =   NUM_PERM
						a					     =   MinHashLSH(threshold=self._duplication_jaccard_threshold		,       num_perm=self._num_perm		)

						a					     =   defaultdict(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self._index.query(lowerCamelCase_		)
						if code_key in self._index.keys:
											print(F'''Duplicate key {code_key}'''		)
											return

						self._index.insert(lowerCamelCase_		,       lowerCamelCase_		)
						if len(lowerCamelCase_		) > 0:
											for base_duplicate in close_duplicates:
																if base_duplicate in self._duplicate_clusters:
																					self._duplicate_clusters[base_duplicate].add(lowerCamelCase_		)
																					break
											else:
																self._duplicate_clusters[close_duplicates[0]].add(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   []
						for base, duplicates in self._duplicate_clusters.items():
											a					     =   [base] + list(lowerCamelCase_		)
											# reformat the cluster to be a list of dict
											a					     =   [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
											duplicate_clusters.append(lowerCamelCase_		)
						return duplicate_clusters




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.get_duplicate_clusters()
						with open(lowerCamelCase_		,       "w"		) as f:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)






def     a(      A       :    Any       )		->       List[Any]:
					"""simple docstring"""





					a     ,				a					     =   element
					a					     =   get_min_hash([t for t in NON_ALPHA.split(data["content"]       ) if len(t.strip()       ) > 0]       )
					if min_hash is not None:
										return (index, data["repo_name"], data["path"]), min_hash






def     a(      A       :    Type[Dataset]       )		->       List[Any]:
					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash ,     ThreadedIterator(A ,     max_queue_size=1_0000       ) ,     chunksize=100 ,     ):
															if data is not None:
																				yield data






def     a(      A       :    Type[Dataset] ,     A       :    float       )		->       Dict:
					"""simple docstring"""





					a					     =   DuplicationIndex(duplication_jaccard_threshold=A       )

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A       )       ) ,     max_queue_size=100       )       ):
										di.add(A ,     A       )

					# Returns a List[Cluster] where Cluster is List[str] with the filenames.
					return di.get_duplicate_clusters()






def     a(      A       :    str ,     A       :    str       )		->       float:
					"""simple docstring"""





					a					     =   get_tokens(A       )
					a					     =   get_tokens(A       )
					return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_lowercase: int      			=		None






def     a(      A       :    str ,     A       :    Tuple       )		->       int:
					"""simple docstring"""





					a					     =   []
					for elementa in cluster:
										a					     =   _shared_dataset[elementa["base_index"]]["content"]
										for elementa in extremes:
															a					     =   _shared_dataset[elementa["base_index"]]["content"]
															if jaccard_similarity(A ,     A       ) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															a					     =   1
															extremes.append(A       )
					return extremes






def     a(      A       :    str ,     A       :    List[str] ,     A       :    int       )		->       Tuple:
					"""simple docstring"""





					global _shared_dataset
					a					     =   dataset
					a					     =   []
					a					     =   partial(_find_cluster_extremes_shared ,     jaccard_threshold=A       )
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        A ,     A ,     ) ,     total=len(A       ) ,     ):
															extremes_list.append(A       )
					return extremes_list







def     a(      A       :    Type[Dataset] ,     A       :    float = 0.85       )		->       Tuple[Type[Dataset], List[List[Dict]]]:
					"""simple docstring"""





					a					     =   make_duplicate_clusters(A ,     A       )
					a					     =   {x["base_index"] for cluster in duplicate_clusters for x in cluster}
					a					     =   {}
					a					     =   find_extremes(A ,     A ,     A       )
					for extremes in extremes_clusters:
										for element in extremes:
															a					     =   element
					a					     =   duplicate_indices - set(extreme_dict.keys()       )
					a					     =   dataset.filter(lambda A ,     A       : idx not in remove_indices ,     with_indices=A       )

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															a					     =   element["base_index"] in extreme_dict
															if element["is_extreme"]:
																				a					     =   extreme_dict[element["base_index"]]["copies"]

					print(f'''Original dataset size: {len(A       )}'''       )
					print(f'''Number of duplicate clusters: {len(A       )}'''       )
					print(f'''Files in duplicate cluster: {len(A       )}'''       )
					print(f'''Unique files in duplicate cluster: {len(A       )}'''       )
					print(f'''Filtered dataset size: {len(A       )}'''       )

					return ds_filter, duplicate_clusters
