


'''simple docstring'''




import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_      :		Dict          =		get_tests_dir("fixtures/test_sentencepiece.model")

if is_sentencepiece_available():
 import sentencepiece as sp


UpperCAmelCase_      :		Optional[int]          =		5
UpperCAmelCase_      :		Optional[Any]          =		10







@require_sentencepiece
@require_tokenizers
class 					a			(				snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		SpeechaTextTokenizer
 __lowerCAmelCase						: List[Any]							  =		False
 __lowerCAmelCase						: str							  =		True




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    super().setUp()

    _a					:  List[Any]						=  sp.SentencePieceProcessor()
    spm_model.Load(lowerCamelCase_      )
    _a					:  Optional[Any]						=  ['<s>', '<pad>', '</s>', '<unk>']

    vocab += [spm_model.IdToPiece(id_      ) for id_ in range(len(lowerCamelCase_      )      )]
    _a					:  List[str]						=  dict(zip(lowerCamelCase_		,      range(len(lowerCamelCase_      )      )      )      )

    _a					:  int						=  Path(self.tmpdirname      )
    save_json(lowerCamelCase_		,      save_dir / VOCAB_FILES_NAMES['vocab_file']      )
    if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(lowerCamelCase_		,      save_dir / VOCAB_FILES_NAMES['spm_file']      )

    _a					:  Dict						=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
    tokenizer.save_pretrained(self.tmpdirname      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Tuple						=  '<pad>'
    _a					:  int						=  1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_      )		,      lowerCamelCase_      )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_      )		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  List[Any]						=  list(self.get_tokenizer().get_vocab().keys()      )

    self.assertEqual(vocab_keys[0]		,      '<s>'      )
    self.assertEqual(vocab_keys[1]		,      '<pad>'      )
    self.assertEqual(vocab_keys[-1]		,      'j'      )
    self.assertEqual(len(lowerCamelCase_      )		,      1_0_0_1      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    self.assertEqual(self.get_tokenizer().vocab_size		,      1_0_0_1      )




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  List[str]						=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )

    _a					:  Dict						=  tokenizer.tokenize('This is a test'      )
    self.assertListEqual(lowerCamelCase_		,      ['▁This', '▁is', '▁a', '▁t', 'est']      )

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(lowerCamelCase_      )		,      [2_8_9, 5_0, 1_4, 1_7_4, 3_8_6]		,      )

    _a					:  List[Any]						=  tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
    self.assertListEqual(
        lowerCamelCase_		,      [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']		,      )
    _a					:  Tuple						=  tokenizer.convert_tokens_to_ids(lowerCamelCase_      )
    self.assertListEqual(lowerCamelCase_		,      [1_2, 2_5, 8_8, 5_9, 2_8, 2_3, 1_1, 4, 6_0_6, 3_5_1, 3_5_1, 3_5_1, 7, 1_6, 7_0, 5_0, 7_6, 8_4, 1_0, 4, 8]      )

    _a					:  List[str]						=  tokenizer.convert_ids_to_tokens(lowerCamelCase_      )
    self.assertListEqual(
        lowerCamelCase_		,      [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']		,      )




 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    # fmt: off
    _a					:  Dict						=  {'input_ids': [[3_7_9_1, 7_9_7, 3_1, 1_1, 6_4, 7_9_7, 3_1, 2_4_2_9, 4_3_3, 1_2, 1_1_7_6, 1_2, 2_0, 7_8_6, 9_1_5, 1_4_2, 2_4_1_3, 2_4_0, 3_7, 3_2_3_8, 7_9_7, 3_1, 1_1, 3_5, 9_3, 9_1_5, 1_4_2, 2_4_1_3, 2_4_0, 3_7, 5_5_4_0, 5_6_7, 1_2_7_6, 9_3, 3_7, 6_1_0, 4_0, 6_2, 4_5_5, 6_5_7, 1_0_4_2, 1_2_3, 7_8_0, 1_7_7, 3_7, 3_0_9, 2_4_1, 1_2_9_8, 5_1_4, 2_0, 2_9_2, 2_7_3_7, 1_1_4, 2_4_6_9, 2_4_1, 8_5, 6_4, 3_0_2, 5_4_8, 5_2_8, 4_2_3, 4, 5_0_9, 4_0_6, 4_2_3, 3_7, 6_0_1, 4, 7_7_7, 3_0_2, 5_4_8, 5_2_8, 4_2_3, 2_8_4, 4, 3_3_8_8, 5_1_1, 4_5_9, 4, 3_5_5_5, 4_0, 3_2_1, 3_0_2, 7_0_5, 4, 3_3_8_8, 5_1_1, 5_8_3, 3_2_6, 5, 5, 5, 6_2, 3_3_1_0, 5_6_0, 1_7_7, 2_6_8_0, 2_1_7, 1_5_0_8, 3_2, 3_1, 8_5_3, 4_1_8, 6_4, 5_8_3, 5_1_1, 1_6_0_5, 6_2, 3_5, 9_3, 5_6_0, 1_7_7, 2_6_8_0, 2_1_7, 1_5_0_8, 1_5_2_1, 6_4, 5_8_3, 5_1_1, 5_1_9, 6_2, 2_0, 1_5_1_5, 7_6_4, 2_0, 1_4_9, 2_6_1, 5_6_2_5, 7_9_7_2, 2_0, 5_5_4_0, 5_6_7, 1_2_7_6, 9_3, 3_9_2_5, 1_6_7_5, 1_1, 1_5, 8_0_2, 7_9_7_2, 5_7_6, 2_1_7, 1_5_0_8, 1_1, 3_5, 9_3, 1_2_5_3, 2_4_4_1, 1_5, 2_8_9, 6_5_2, 3_1, 4_1_6, 3_2_1, 3_8_4_2, 1_1_5, 4_0, 9_1_1, 8, 4_7_6, 6_1_9, 4, 3_8_0, 1_4_2, 4_2_3, 3_3_5, 2_4_0, 3_5, 9_3, 2_6_4, 8, 1_1, 3_3_5, 5_6_9, 4_2_0, 1_6_3, 5, 2], [2_6_0, 5_4_8, 5_2_8, 4_2_3, 2_0, 4_5_1, 2_0, 2_6_8_1, 1_1_5_3, 3_4_3_4, 2_0, 5_5_4_0, 3_7, 5_6_7, 1_2_6, 1_2_5_3, 2_4_4_1, 3_3_7_6, 4_4_9, 2_1_0, 4_3_1, 1_5_6_3, 1_7_7, 7_6_7, 5_5_4_0, 1_1, 1_2_0_3, 4_7_2, 1_1, 2_9_5_3, 6_8_5, 2_8_5, 3_6_4, 7_0_6, 1_1_5_3, 2_0, 6_7_9_9, 2_0, 2_8_6_9, 2_0, 4_4_6_4, 1_2_6, 4_0, 2_4_2_9, 2_0, 1_0_4_0, 8_6_6, 2_6_6_4, 4_1_8, 2_0, 3_1_8, 2_0, 1_7_2_6, 1_8_6, 2_0, 2_6_5, 5_2_2, 3_5, 9_3, 2_1_9_1, 4_6_3_4, 2_0, 1_0_4_0, 1_2, 6_7_9_9, 1_5, 2_2_8, 2_3_5_6, 1_4_2, 3_1, 1_1, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_7_5, 2_6_6_6, 6_8_4, 1_5_8_2, 1_1_7_6, 1_2, 6_2_7, 1_4_9, 6_1_9, 2_0, 4_9_0_2, 5_6_3, 1_1, 2_0, 1_4_9, 2_6_1, 3_4_2_0, 2_3_5_6, 1_7_4, 1_4_2, 4_7_1_4, 1_3_1, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=lowerCamelCase_		,      model_name='facebook/s2t-small-mustc-en-de-st'		,      revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad'		,      )







@require_sentencepiece
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: List[Any]							  =		"""valhalla/s2t_mustc_multilinguial_medium"""

 __lowerCAmelCase						: Dict							  =		"""C'est trop cool"""
 __lowerCAmelCase						: str							  =		"""Esto es genial"""




 @classmethod
 def   __UpperCamelCase   (    cls      )      ->    str:
    _a					:  SpeechaTextTokenizer						=  SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name      )
    return cls




 def   __UpperCamelCase   (    self      )      ->    int:
    self.assertEqual(self.tokenizer.lang_code_to_id['pt']		,      4      )
    self.assertEqual(self.tokenizer.lang_code_to_id['ru']		,      6      )
    self.assertEqual(self.tokenizer.lang_code_to_id['it']		,      9      )
    self.assertEqual(self.tokenizer.lang_code_to_id['de']		,      1_1      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    self.assertEqual(self.tokenizer.vocab_size		,      1_0_0_0_0      )




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    self.assertIn(lowerCamelCase_		,      self.tokenizer.all_special_ids      )
    _a					:  List[Any]						=  [ES_CODE, 4, 1_6_0_1, 4_7, 7_6_4_7, 2]
    _a					:  int						=  self.tokenizer.decode(lowerCamelCase_		,      skip_special_tokens=lowerCamelCase_      )
    _a					:  List[str]						=  self.tokenizer.decode(generated_ids[1:]		,      skip_special_tokens=lowerCamelCase_      )
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )
    self.assertNotIn(self.tokenizer.eos_token		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  'fr'
    _a					:  int						=  self.tokenizer(self.french_text      ).input_ids
    self.assertEqual(encoded[0]		,      lowerCamelCase_      )
    self.assertEqual(encoded[-1]		,      self.tokenizer.eos_token_id      )




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a					:  Dict						=  'fr'
    self.assertListEqual(self.tokenizer.prefix_tokens		,      [FR_CODE]      )

    _a					:  int						=  'es'
    self.assertListEqual(self.tokenizer.prefix_tokens		,      [ES_CODE]      )






'''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_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    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  str						=  'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   _a					:  Any						=  Image.open(requests.get(A		,	stream=A      ).raw      ).convert('RGB'      )

   return image


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  []
   # 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    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Dict						=  dct.pop(A      )
   _a					:  Tuple						=  val


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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

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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  int						=  3_6_4 if 'coco' in model_name else 2_2_4
   _a					:  List[Any]						=  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:
      _a					:  Optional[Any]						=  TaConfig.from_pretrained('google/flan-t5-xl'		,	dense_act_fn='gelu'		,	bos_token_id=1      ).to_dict()
   elif "t5-xxl" in model_name:
      _a					:  Tuple						=  TaConfig.from_pretrained('google/flan-t5-xxl'		,	dense_act_fn='gelu'		,	bos_token_id=1      ).to_dict()
   elif "vicuna-7b" in model_name:
      _a					:  int						=  LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'		,	vocab_size=3_2_0_0_1      ).to_dict()
   elif "vicuna-13b" in model_name:
      _a					:  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
   _a					:  Tuple						=  InstructBlipQFormerConfig(vocab_size=3_0_5_2_3      ).to_dict()
   _a					:  Optional[Any]						=  InstructBlipConfig(vision_config=A		,	text_config=A		,	qformer_config=A      )

   return config, image_size


@torch.no_grad()
def    UpperCAmelCase_  (       A		,	A=None		,	A=False      ):
   '''simple docstring'''





   _a					:  Tuple						=  AutoTokenizer.from_pretrained('bert-base-uncased'		,	truncation_side='left'      )
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}      )

   if "t5" in model_name:
      _a					:  int						=  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>"})
      _a					:  Optional[Any]						=  LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'		,	truncation_side='left'		,	bos_token='</s>'		,	unk_token='</s>'      )
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}      )

   _a		,						_a					:  Tuple						=  get_blipa_config(A      )
   _a					:  Optional[Any]						=  InstructBlipForConditionalGeneration(A      ).eval()

   _a					:  Union[str, Any]						=  {
       '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'),
   }

   _a		,						_a					:  Dict						=  model_name_to_original[model_name]

   # load original model
   print('Loading original model...'      )
   _a					:  int						=  'cuda:1' if torch.cuda.is_available() else 'cpu'
   _a					:  Any						=  'cuda:2' if torch.cuda.is_available() else 'cpu'
   _a		,						_a		,						_a					:  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
   _a					:  Tuple						=  original_model.state_dict()
   _a					:  List[Any]						=  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					:  Union[str, Any]						=  state_dict.pop(A      )
      if key.startswith('Qformer.bert'      ):
         _a					:  Union[str, Any]						=  key.replace('Qformer.bert'		,	'qformer'      )
      if "attention.self" in key:
         _a					:  List[Any]						=  key.replace('self'		,	'attention'      )
      if "llm_proj" in key:
         _a					:  int						=  key.replace('llm_proj'		,	'language_projection'      )
      if "t5_proj" in key:
         _a					:  str						=  key.replace('t5_proj'		,	'language_projection'      )
      if key.startswith('llm_model'      ):
         _a					:  int						=  key.replace('llm_model'		,	'language_model'      )
      if key.startswith('t5'      ):
         _a					:  Tuple						=  key.replace('t5'		,	'language'      )
      _a					:  Optional[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      )

   _a					:  Dict						=  load_demo_image()
   _a					:  Any						=  'What is unusual about this image?'

   # create processor
   _a					:  Any						=  BlipImageProcessor(
       size={'height': image_size, 'width': image_size}		,	image_mean=A		,	image_std=A      )
   _a					:  Dict						=  InstructBlipProcessor(
       image_processor=A		,	tokenizer=A		,	qformer_tokenizer=A		,	)
   _a					:  str						=  processor(images=A		,	text=A		,	return_tensors='pt'      ).to(A      )

   # make sure processor creates exact same pixel values
   _a					:  str						=  vis_processors['eval'](A      ).unsqueeze(0      ).to(A      )
   _a					:  Optional[int]						=  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:
         _a					:  Any						=  original_model({'image': original_pixel_values, 'text_input': [prompt]}      ).logits
         _a					:  str						=  hf_model(**A      ).logits
      else:
         _a					:  Optional[Any]						=  original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}      ).logits
         _a					:  List[Any]						=  tokenizer('\n'		,	return_tensors='pt'      ).input_ids.to(A      )
         _a					:  str						=  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,	-1_0_0      )
         _a					:  List[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
   _a					:  str						=  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...'      )
   _a					:  Optional[int]						=  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...'      )
   _a					:  Dict						=  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?
      _a					:  Tuple						=  2
   print('Original generation:'		,	A      )
   _a					:  Dict						=  processor.batch_decode(A		,	skip_special_tokens=A      )
   _a					:  Tuple						=  [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__":
 UpperCAmelCase_      :		Optional[int]          =		argparse.ArgumentParser()
 UpperCAmelCase_      :		List[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",
 )

 UpperCAmelCase_      :		Optional[int]          =		parser.parse_args()

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


