
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_UpperCAmelCase :			int              =	get_logger(__name__)


class     lowercase :
		__lowercase	:	List[str]     					=    "dummy_data"
		__lowercase	:	int     					=    "datasets"
		__lowercase	:	List[str]     					=    False



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				A_ = None    ,				A_ = False    ,				A_ = True    ,				A_ = None    ,				)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					0
				UpperCamelCase          =					dataset_name
				UpperCamelCase          =					cache_dir
				UpperCamelCase          =					use_local_dummy_data
				UpperCamelCase          =					config
				# download_callbacks take a single url as input
				UpperCamelCase          =					download_callbacks or []
				# if False, it doesn't load existing files and it returns the paths of the dummy files relative
				# to the dummy_data zip file root
				UpperCamelCase          =					load_existing_dummy_data

				# TODO(PVP, QL) might need to make this more general
				UpperCamelCase          =					str(A_	)
				# to be downloaded
				UpperCamelCase          =					None
				UpperCamelCase          =					None



		@property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				if self._dummy_file is None:
						UpperCamelCase          =					self.download_dummy_data()
				return self._dummy_file



		@property
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				if self.config is not None:
						# structure is dummy / config_name / version_name
						return os.path.join('dummy'    ,				self.config.name    ,				self.version_name	)
				# structure is dummy / version_name
				return os.path.join('dummy'    ,				self.version_name	)



		@property
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				return os.path.join(self.dummy_data_folder    ,				'dummy_data.zip'	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					(
				    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
				)

				UpperCamelCase          =					cached_path(
				    A_    ,				cache_dir=self.cache_dir    ,				extract_compressed_file=A_    ,				force_extract=A_	)

				return os.path.join(A_    ,				self.dummy_file_name	)



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return os.path.join(self.datasets_scripts_dir    ,				self.dataset_name    ,				self.dummy_zip_file	)



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				if self._bucket_url is None:
						UpperCamelCase          =					hf_github_url(self.dataset_name    ,				self.dummy_zip_file.replace(os.sep    ,				'/'	)	)
				return self._bucket_url



		@property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# return full path if its a dir
				if os.path.isdir(self.dummy_file	):
						return self.dummy_file
				# else cut off path to file -> example `xsum`.
				return "/".join(self.dummy_file.replace(os.sep    ,				'/'	).split('/'	)[:-1]	)



		def __UpperCamelCase			(			self    ,				A_    ,				*A_	)  -> Optional[Any]:
				"""simple docstring"""
				if self.load_existing_dummy_data:
						# dummy data is downloaded and tested
						UpperCamelCase          =					self.dummy_file
				else:
						# dummy data cannot be downloaded and only the path to dummy file is returned
						UpperCamelCase          =					self.dummy_file_name

				# special case when data_url is a dict
				if isinstance(A_    ,				A_	):
						return self.create_dummy_data_dict(A_    ,				A_	)
				elif isinstance(A_    ,				(list, tuple)	):
						return self.create_dummy_data_list(A_    ,				A_	)
				else:
						return self.create_dummy_data_single(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				*A_	)  -> Tuple:
				"""simple docstring"""
				return self.download_and_extract(A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				return self.download_and_extract(A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				*A_    ,				**A_	)  -> Optional[int]:
				"""simple docstring"""
				return path



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return {}



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					{}
				for key, single_urls in data_url.items():
						for download_callback in self.download_callbacks:
								if isinstance(A_    ,				A_	):
										for single_url in single_urls:
												download_callback(A_	)
								else:
										UpperCamelCase          =					single_urls
										download_callback(A_	)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
						if isinstance(A_    ,				A_	):
								UpperCamelCase          =					[os.path.join(A_    ,				urllib.parse.quote_plus(Path(A_	).name	)	) for x in single_urls]
						else:
								UpperCamelCase          =					single_urls
								UpperCamelCase          =					os.path.join(A_    ,				urllib.parse.quote_plus(Path(A_	).name	)	)
						UpperCamelCase          =					value

				# make sure that values are unique
				if all(isinstance(A_    ,				A_	) for i in dummy_data_dict.values()	) and len(set(dummy_data_dict.values()	)	) < len(
				    dummy_data_dict.values()	):
						# append key to value to make its name unique
						UpperCamelCase          =					{key: value + key for key, value in dummy_data_dict.items()}

				return dummy_data_dict



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					[]
				# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
				UpperCamelCase          =					all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'    ,				A_	)	) for url in data_url	)
				UpperCamelCase          =					all(
				    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'	) for url in data_url	)
				if data_url and (is_tf_records or is_pubmed_records):
						UpperCamelCase          =					[data_url[0]] * len(A_	)
				for single_url in data_url:
						for download_callback in self.download_callbacks:
								download_callback(A_	)
						# we force the name of each key to be the last file / folder name of the url path
						# if the url has arguments, we need to encode them with urllib.parse.quote_plus
						UpperCamelCase          =					os.path.join(A_    ,				urllib.parse.quote_plus(single_url.split('/'	)[-1]	)	)
						dummy_data_list.append(A_	)
				return dummy_data_list



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Tuple:
				"""simple docstring"""
				for download_callback in self.download_callbacks:
						download_callback(A_	)
				# we force the name of each key to be the last file / folder name of the url path
				# if the url has arguments, we need to encode them with urllib.parse.quote_plus
				UpperCamelCase          =					os.path.join(A_    ,				urllib.parse.quote_plus(data_url.split('/'	)[-1]	)	)
				if os.path.exists(A_	) or not self.load_existing_dummy_data:
						return value
				else:
						# Backward compatibility, maybe deprecate at one point.
						# For many datasets with single url calls to dl_manager.download_and_extract,
						# the dummy_data.zip file is actually the zipped downloaded file
						# while now we expected the dummy_data.zip file to be a directory containing
						# the downloaded file.
						return path_to_dummy_data



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""
				def _iter_archive_members(A_	):
						# this preserves the order of the members inside the ZIP archive
						UpperCamelCase          =					Path(self.dummy_file	).parent
						UpperCamelCase          =					path.relative_to(A_	)
						with ZipFile(self.local_path_to_dummy_data	) as zip_file:
								UpperCamelCase          =					zip_file.namelist()
						for member in members:
								if member.startswith(relative_path.as_posix()	):
										yield dummy_parent_path.joinpath(A_	)

				UpperCamelCase          =					Path(A_	)
				UpperCamelCase          =					_iter_archive_members(A_	) if self.use_local_dummy_data else path.rglob('*'	)
				for file_path in file_paths:
						if file_path.is_file() and not file_path.name.startswith(('.', '__')	):
								yield file_path.relative_to(A_	).as_posix(), file_path.open('rb'	)



		def __UpperCamelCase			(			self    ,				A_	)  -> int:
				"""simple docstring"""
				if not isinstance(A_    ,				A_	):
						UpperCamelCase          =					[paths]
				for path in paths:
						if os.path.isfile(A_	):
								if os.path.basename(A_	).startswith(('.', '__')	):
										return
								yield path
						else:
								for dirpath, dirnames, filenames in os.walk(A_	):
										if os.path.basename(A_	).startswith(('.', '__')	):
												continue
										dirnames.sort()
										for filename in sorted(A_	):
												if filename.startswith(('.', '__')	):
														continue
												yield os.path.join(A_    ,				A_	)


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	( )       ->  int:
		'''simple docstring'''

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

		return image


def 				A	( lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# 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	( lowercase   ,	lowercase   ,	lowercase			)       ->  Dict:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase			)       ->  List[str]:
		'''simple docstring'''

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

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


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

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

		UpperCamelCase          =					BlipaConfig(vision_config=lowercase   ,	text_config=lowercase			)

		return config, image_size


@torch.no_grad()
def 				A	( lowercase   ,	lowercase=None   ,	lowercase=False			)       ->  Union[str, Any]:
		'''simple docstring'''

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

		UpperCamelCase          =					BlipaForConditionalGeneration(lowercase			).eval()

		UpperCamelCase          =					{
		    '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'),
		}

		UpperCamelCase     ,  UpperCamelCase          =					model_name_to_original[model_name]

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

		# update state dict keys
		UpperCamelCase          =					original_model.state_dict()
		UpperCamelCase          =					create_rename_keys(lowercase			)
		for src, dest in rename_keys:
				rename_key(lowercase   ,	lowercase   ,	lowercase			)

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				UpperCamelCase          =					state_dict.pop(lowercase			)
				if key.startswith('Qformer.bert'			):
						UpperCamelCase          =					key.replace('Qformer.bert'   ,	'qformer'			)
				if "attention.self" in key:
						UpperCamelCase          =					key.replace('self'   ,	'attention'			)
				if "opt_proj" in key:
						UpperCamelCase          =					key.replace('opt_proj'   ,	'language_projection'			)
				if "t5_proj" in key:
						UpperCamelCase          =					key.replace('t5_proj'   ,	'language_projection'			)
				if key.startswith('opt'			):
						UpperCamelCase          =					key.replace('opt'   ,	'language'			)
				if key.startswith('t5'			):
						UpperCamelCase          =					key.replace('t5'   ,	'language'			)
				UpperCamelCase          =					val

		# read in qv biases
		read_in_q_v_bias(lowercase   ,	lowercase			)

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

		UpperCamelCase          =					load_demo_image()
		UpperCamelCase          =					vis_processors['eval'](lowercase			).unsqueeze(0			).to(lowercase			)
		UpperCamelCase          =					tokenizer(['\n']   ,	return_tensors='pt'			).input_ids.to(lowercase			)

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

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

		original_model.to(lowercase			)
		hf_model.to(lowercase			)
		with torch.no_grad():
				if "opt" in model_name:
						UpperCamelCase          =					original_model({'image': original_pixel_values, 'text_input': ['']}			).logits
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase			).logits
				else:
						UpperCamelCase          =					original_model(
						    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}			).logits
						UpperCamelCase          =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id   ,	-100			)
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase   ,	labels=lowercase			).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":
				UpperCamelCase          =					torch.tensor(
				    [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]   ,	device=lowercase			)
				assert torch.allclose(logits[0, :3, :3]   ,	lowercase   ,	atol=1e-4			)
		elif model_name == "blip2-flan-t5-xl-coco":
				UpperCamelCase          =					torch.tensor(
				    [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]   ,	device=lowercase			)
		else:
				# cast to same type
				UpperCamelCase          =					logits.dtype
				assert torch.allclose(original_logits.to(lowercase			)   ,	lowercase   ,	atol=1e-2			)
		print('Looks ok!'			)

		print('Generating a caption...'			)
		UpperCamelCase          =					''
		UpperCamelCase          =					tokenizer(lowercase   ,	return_tensors='pt'			).input_ids.to(lowercase			)

		UpperCamelCase          =					original_model.generate({'image': original_pixel_values}			)
		UpperCamelCase          =					hf_model.generate(
		    lowercase   ,	lowercase   ,	do_sample=lowercase   ,	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:'   ,	lowercase			)
		UpperCamelCase          =					input_ids.shape[1]
		UpperCamelCase          =					processor.batch_decode(outputs[:, prompt_length:]   ,	skip_special_tokens=lowercase			)
		UpperCamelCase          =					[text.strip() for text in output_text]
		print('HF generation:'   ,	lowercase			)

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

		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 :			Optional[int]              =	argparse.ArgumentParser()
					_UpperCAmelCase :			str              =	[
					    "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 :			List[str]              =	parser.parse_args()

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