


import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowerCAmelCase__  :Tuple             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

lowerCAmelCase__  :List[Any]             =							{
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

lowerCAmelCase__  :Tuple             =							{
    '''jukebox''': 5_1_2,
}

class 		__a				(    UpperCAmelCase      ):
		_a     :  List[Any]           =     VOCAB_FILES_NAMES
		_a     :  List[Any]           =     PRETRAINED_VOCAB_FILES_MAP
		_a     :  Any           =     PRETRAINED_LYRIC_TOKENS_SIZES
		_a     :  str           =     ['input_ids', 'attention_mask']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=["v3", "v2", "v2"]      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=5      ,   _SCREAMING_SNAKE_CASE="<|endoftext|>"      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      AddedToken(_SCREAMING_SNAKE_CASE      ,   lstrip=_SCREAMING_SNAKE_CASE      ,   rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) else unk_token
									super().__init__(
									    unk_token=_SCREAMING_SNAKE_CASE      ,   n_genres=_SCREAMING_SNAKE_CASE      ,   version=_SCREAMING_SNAKE_CASE      ,   max_n_lyric_tokens=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )
									_UpperCAmelCase					=      version
									_UpperCAmelCase					=      max_n_lyric_tokens
									_UpperCAmelCase					=      n_genres

									with open(_SCREAMING_SNAKE_CASE      ,   encoding='utf-8'						) as vocab_handle:
																_UpperCAmelCase					=      json.load(_SCREAMING_SNAKE_CASE						)

									with open(_SCREAMING_SNAKE_CASE      ,   encoding='utf-8'						) as vocab_handle:
																_UpperCAmelCase					=      json.load(_SCREAMING_SNAKE_CASE						)

									with open(_SCREAMING_SNAKE_CASE      ,   encoding='utf-8'						) as vocab_handle:
																_UpperCAmelCase					=      json.load(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
									# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
									if len(self.lyrics_encoder						) == 79:
																_UpperCAmelCase					=      oov.replace(R'\-\''      ,   R'\-+\''						)

									_UpperCAmelCase					=      regex.compile(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      {v: k for k, v in self.artists_encoder.items()}
									_UpperCAmelCase					=      {v: k for k, v in self.genres_encoder.items()}
									_UpperCAmelCase					=      {v: k for k, v in self.lyrics_encoder.items()}
		@property
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									return len(self.artists_encoder						) + len(self.genres_encoder						) + len(self.lyrics_encoder						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									return dict(self.artists_encoder      ,   self.genres_encoder      ,   self.lyrics_encoder						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      [self.artists_encoder.get(_SCREAMING_SNAKE_CASE      ,   0						) for artist in list_artists]
									for genres in range(len(_SCREAMING_SNAKE_CASE						)						):
																_UpperCAmelCase					=      [self.genres_encoder.get(_SCREAMING_SNAKE_CASE      ,   0						) for genre in list_genres[genres]]
																_UpperCAmelCase					=      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]						))

									_UpperCAmelCase					=      [[self.lyrics_encoder.get(_SCREAMING_SNAKE_CASE      ,   0						) for character in list_lyrics[0]], [], []]
									return artists_id, list_genres, lyric_ids
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									return list(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self.prepare_for_tokenization(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self._tokenize(_SCREAMING_SNAKE_CASE						)
									return artist, genre, lyrics
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = False						)			->	Tuple[str, str, str, Dict[str, Any]]:




									"""simple docstring"""





									for idx in range(len(self.version						)						):
																if self.version[idx] == "v3":
																							_UpperCAmelCase					=      artists[idx].lower()
																							_UpperCAmelCase					=      [genres[idx].lower()]
																else:
																							_UpperCAmelCase					=      self._normalize(artists[idx]						) + '.v2'
																							_UpperCAmelCase					=      [
																							    self._normalize(_SCREAMING_SNAKE_CASE						) + '.v2' for genre in genres[idx].split('_'						)
																							]  # split is for the full dictionary with combined genres

									if self.version[0] == "v2":
																_UpperCAmelCase					=      regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'						)
																_UpperCAmelCase					=      'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
																_UpperCAmelCase					=      {vocab[index]: index + 1 for index in range(len(_SCREAMING_SNAKE_CASE						)						)}
																_UpperCAmelCase					=      0
																_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						) + 1
																_UpperCAmelCase					=      self.vocab
																_UpperCAmelCase					=      {v: k for k, v in self.vocab.items()}
																_UpperCAmelCase					=      ''
									else:
																_UpperCAmelCase					=      regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'						)

									_UpperCAmelCase					=      self._run_strip_accents(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      lyrics.replace('\\'      ,   '\n'						)
									_UpperCAmelCase					=      self.out_of_vocab.sub(''      ,   _SCREAMING_SNAKE_CASE						), [], []
									return artists, genres, lyrics
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      unicodedata.normalize('NFD'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      []
									for char in text:
																_UpperCAmelCase					=      unicodedata.category(_SCREAMING_SNAKE_CASE						)
																if cat == "Mn":
																							continue
																output.append(_SCREAMING_SNAKE_CASE						)
									return "".join(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      (
									    [chr(_SCREAMING_SNAKE_CASE						) for i in range(ord('a'						)      ,   ord('z'						) + 1						)]
									    + [chr(_SCREAMING_SNAKE_CASE						) for i in range(ord('A'						)      ,   ord('Z'						) + 1						)]
									    + [chr(_SCREAMING_SNAKE_CASE						) for i in range(ord('0'						)      ,   ord('9'						) + 1						)]
									    + ['.']
									)
									_UpperCAmelCase					=      frozenset(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      re.compile(R'_+'						)
									_UpperCAmelCase					=      ''.join([c if c in accepted else '_' for c in text.lower()]						)
									_UpperCAmelCase					=      pattern.sub('_'      ,   _SCREAMING_SNAKE_CASE						).strip('_'						)
									return text
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									return " ".join(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False						)			->	List[str]:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      TensorType(_SCREAMING_SNAKE_CASE						)

									# Get a function reference for the correct framework
									if tensor_type == TensorType.TENSORFLOW:
																if not is_tf_available():
																							raise ImportError(
																							    'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'						)
																import tensorflow as tf

																_UpperCAmelCase					=      tf.constant
																_UpperCAmelCase					=      tf.is_tensor
									elif tensor_type == TensorType.PYTORCH:
																if not is_torch_available():
																							raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'						)
																import torch

																_UpperCAmelCase					=      torch.tensor
																_UpperCAmelCase					=      torch.is_tensor
									elif tensor_type == TensorType.JAX:
																if not is_flax_available():
																							raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'						)
																import jax.numpy as jnp  # noqa: F811

																_UpperCAmelCase					=      jnp.array
																_UpperCAmelCase					=      _is_jax
									else:
																_UpperCAmelCase					=      np.asarray
																_UpperCAmelCase					=      _is_numpy

									# Do the tensor conversion in batch

									try:
																if prepend_batch_axis:
																							_UpperCAmelCase					=      [inputs]

																if not is_tensor(_SCREAMING_SNAKE_CASE						):
																							_UpperCAmelCase					=      as_tensor(_SCREAMING_SNAKE_CASE						)
									except:  # noqa E722
																raise ValueError(
																    'Unable to create tensor, you should probably activate truncation and/or padding '
																    'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'						)

									return inputs
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=""      ,   _SCREAMING_SNAKE_CASE="pt"						)			->	BatchEncoding:




									"""simple docstring"""





									_UpperCAmelCase					=      [0, 0, 0]
									_UpperCAmelCase					=      [artist] * len(self.version						)
									_UpperCAmelCase					=      [genres] * len(self.version						)

									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self.tokenize(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self._convert_token_to_id(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      [-INFINITY] * len(full_tokens[-1]						)
									_UpperCAmelCase					=      [
									    self.convert_to_tensors(
									        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
									    for i in range(len(self.version						)						)
									]
									return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None						)			->	Tuple[str]:




									"""simple docstring"""





									if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																return

									_UpperCAmelCase					=      os.path.join(
									    _SCREAMING_SNAKE_CASE      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']						)
									with open(_SCREAMING_SNAKE_CASE      ,   'w'      ,   encoding='utf-8'						) as f:
																f.write(json.dumps(self.artists_encoder      ,   ensure_ascii=_SCREAMING_SNAKE_CASE						)						)

									_UpperCAmelCase					=      os.path.join(
									    _SCREAMING_SNAKE_CASE      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']						)
									with open(_SCREAMING_SNAKE_CASE      ,   'w'      ,   encoding='utf-8'						) as f:
																f.write(json.dumps(self.genres_encoder      ,   ensure_ascii=_SCREAMING_SNAKE_CASE						)						)

									_UpperCAmelCase					=      os.path.join(
									    _SCREAMING_SNAKE_CASE      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']						)
									with open(_SCREAMING_SNAKE_CASE      ,   'w'      ,   encoding='utf-8'						) as f:
																f.write(json.dumps(self.lyrics_encoder      ,   ensure_ascii=_SCREAMING_SNAKE_CASE						)						)

									return (artists_file, genres_file, lyrics_file)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.artists_decoder.get(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      [self.genres_decoder.get(_SCREAMING_SNAKE_CASE						) for genre in genres_index]
									_UpperCAmelCase					=      [self.lyrics_decoder.get(_SCREAMING_SNAKE_CASE						) for character in lyric_index]
									return artist, genres, lyrics




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 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).raw	).convert('RGB'	)

							_UpperCAmelCase					=      transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)      ,  interpolation=InterpolationMode.BICUBIC	),
							        transforms.ToTensor(),
							        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)      ,  (0.26_862_954, 0.26_130_258, 0.27_577_711)	),
							    ]	)
							_UpperCAmelCase					=      transform(a__	).unsqueeze(0	).to(a__	)
							return image

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


							'''simple docstring'''





							if "visual_encoder" in key:
														_UpperCAmelCase					=      re.sub('visual_encoder*'      ,  'vision_model.encoder'      ,  a__	)
							if "blocks" in key:
														_UpperCAmelCase					=      re.sub(R'blocks'      ,  'layers'      ,  a__	)
							if "attn" in key:
														_UpperCAmelCase					=      re.sub(R'attn'      ,  'self_attn'      ,  a__	)
							if "norm1" in key:
														_UpperCAmelCase					=      re.sub(R'norm1'      ,  'layer_norm1'      ,  a__	)
							if "norm2" in key:
														_UpperCAmelCase					=      re.sub(R'norm2'      ,  'layer_norm2'      ,  a__	)
							if "encoder.norm" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.norm'      ,  'post_layernorm'      ,  a__	)
							if "encoder.patch_embed.proj" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.patch_embed.proj'      ,  'embeddings.patch_embedding'      ,  a__	)

							if "encoder.pos_embed" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.pos_embed'      ,  'embeddings.position_embedding'      ,  a__	)
							if "encoder.cls_token" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.cls_token'      ,  'embeddings.class_embedding'      ,  a__	)

							if "self_attn" in key:
														_UpperCAmelCase					=      re.sub(R'self_attn.proj'      ,  'self_attn.projection'      ,  a__	)

							return key

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]=None	)  ->		Optional[Any]:


							'''simple docstring'''





							if config_path is not None:
														_UpperCAmelCase					=      BlipConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      BlipConfig(projection_dim=5_1_2      ,  text_config={}      ,  vision_config={}	)

							_UpperCAmelCase					=      BlipForConditionalGeneration(a__	).eval()

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

							_UpperCAmelCase					=      blip_decoder(pretrained=a__      ,  image_size=3_8_4      ,  vit='base'	)
							_UpperCAmelCase					=      pt_model.eval()

							_UpperCAmelCase					=      pt_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							hf_model.load_state_dict(a__	)

							_UpperCAmelCase					=      3_8_4
							_UpperCAmelCase					=      load_demo_image(image_size=a__      ,  device='cpu'	)
							_UpperCAmelCase					=      BertTokenizer.from_pretrained('bert-base-uncased'	)
							_UpperCAmelCase					=      tokenizer(['a picture of']	).input_ids

							_UpperCAmelCase					=      hf_model.generate(a__      ,  a__	)

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

							_UpperCAmelCase					=      hf_model.generate(a__	)

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

							if pytorch_dump_folder_path is not None:
														hf_model.save_pretrained(a__	)

							# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
							_UpperCAmelCase					=      (
							    'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
							)

							_UpperCAmelCase					=      blip_vqa(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							vqa_model.eval()

							_UpperCAmelCase					=      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForQuestionAnswering(a__	)

							hf_vqa_model.load_state_dict(a__	)

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

							_UpperCAmelCase					=      hf_vqa_model.generate(a__      ,  a__	)
							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'	)

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

							_UpperCAmelCase					=      blip_itm(pretrained=a__      ,  image_size=a__      ,  vit='base'	)
							itm_model.eval()

							_UpperCAmelCase					=      itm_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForImageTextRetrieval(a__	)

							_UpperCAmelCase					=      ['A picture of a woman with a dog sitting in a beach']
							_UpperCAmelCase					=      tokenizer(
							    a__      ,  return_tensors='pt'      ,  padding='max_length'      ,  truncation=a__      ,  max_length=3_5      ,  ).input_ids

							hf_itm_model.load_state_dict(a__	)
							hf_itm_model.eval()

							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)
							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)

							assert out[0].item() == 0.2_110_687_494_277_954
							assert torch.nn.functional.softmax(out_itm[0]      ,  dim=1	)[:, 1].item() == 0.45_698_845_386_505_127

							if pytorch_dump_folder_path is not None:
														hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'	)


if __name__ == "__main__":
		lowerCAmelCase__  :int             =							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''')
		lowerCAmelCase__  :List[Any]             =							parser.parse_args()

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