


'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







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




 __lowerCAmelCase						: List[str]							  =		LongformerTokenizer
 __lowerCAmelCase						: str							  =		True
 __lowerCAmelCase						: int							  =		LongformerTokenizerFast
 __lowerCAmelCase						: Dict							  =		True




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    _a					:  List[str]						=  [
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        '\u0120',
        '\u0120l',
        '\u0120n',
        '\u0120lo',
        '\u0120low',
        'er',
        '\u0120lowest',
        '\u0120newer',
        '\u0120wider',
        '<unk>',
    ]
    _a					:  Dict						=  dict(zip(lowerCamelCase_		,      range(len(lowerCamelCase_      )      )      )      )
    _a					:  Any						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
    _a					:  int						=  {'unk_token': '<unk>'}

    _a					:  Union[str, Any]						=  os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['vocab_file']      )
    _a					:  Tuple						=  os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['merges_file']      )
    with open(self.vocab_file		,      'w'		,      encoding='utf-8'      ) as fp:
       fp.write(json.dumps(lowerCamelCase_      ) + '\n'      )
    with open(self.merges_file		,      'w'		,      encoding='utf-8'      ) as fp:
       fp.write('\n'.join(lowerCamelCase_      )      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    int:
    kwargs.update(self.special_tokens_map      )
    return self.tokenizer_class.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      **lowerCamelCase_      )      ->    int:
    kwargs.update(self.special_tokens_map      )
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,      **lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Optional[Any]:
    _a					:  List[str]						=  'lower newer'
    _a					:  Union[str, Any]						=  'lower newer'
    return input_text, output_text




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  int						=  self.tokenizer_class(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map      )
    _a					:  str						=  'lower newer'
    _a					:  Any						=  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
    _a					:  List[str]						=  tokenizer.tokenize(lowerCamelCase_      )  # , add_prefix_space=True)
    self.assertListEqual(lowerCamelCase_		,      lowerCamelCase_      )

    _a					:  str						=  tokens + [tokenizer.unk_token]
    _a					:  Optional[int]						=  [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_      )		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Optional[Any]						=  self.get_tokenizer()

    self.assertListEqual(tokenizer.encode('Hello world!'		,      add_special_tokens=lowerCamelCase_      )		,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]      )
    self.assertListEqual(
        tokenizer.encode('Hello world! cécé herlolip 418'		,      add_special_tokens=lowerCamelCase_      )		,      [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]		,      )




 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Union[str, Any]						=  self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'      )

    _a					:  int						=  tokenizer.encode('sequence builders'		,      add_special_tokens=lowerCamelCase_      )
    _a					:  Union[str, Any]						=  tokenizer.encode('multi-sequence build'		,      add_special_tokens=lowerCamelCase_      )

    _a					:  Dict						=  tokenizer.encode(
        'sequence builders'		,      add_special_tokens=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_      )
    _a					:  Dict						=  tokenizer.encode(
        'sequence builders'		,      'multi-sequence build'		,      add_special_tokens=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_      )

    _a					:  Tuple						=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      )
    _a					:  List[Any]						=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_		,      lowerCamelCase_      )

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  int						=  self.get_tokenizer()

    _a					:  List[str]						=  'Encode this sequence.'
    _a					:  str						=  tokenizer.byte_encoder[' '.encode('utf-8'      )[0]]

    # Testing encoder arguments
    _a					:  Dict						=  tokenizer.encode(lowerCamelCase_		,      add_special_tokens=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_      )
    _a					:  Optional[int]						=  tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
    self.assertNotEqual(lowerCamelCase_		,      lowerCamelCase_      )

    _a					:  List[str]						=  tokenizer.encode(lowerCamelCase_		,      add_special_tokens=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_      )
    _a					:  List[Any]						=  tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )

    tokenizer.add_special_tokens({'bos_token': '<s>'}      )
    _a					:  List[str]						=  tokenizer.encode(lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
    _a					:  List[str]						=  tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
    self.assertNotEqual(lowerCamelCase_		,      lowerCamelCase_      )

    # Testing spaces after special tokens
    _a					:  Dict						=  '<mask>'
    tokenizer.add_special_tokens(
        {'mask_token': AddedToken(lowerCamelCase_		,      lstrip=lowerCamelCase_		,      rstrip=lowerCamelCase_      )}      )  # mask token has a left space
    _a					:  List[str]						=  tokenizer.convert_tokens_to_ids(lowerCamelCase_      )

    _a					:  Optional[int]						=  'Encode <mask> sequence'
    _a					:  int						=  'Encode <mask>sequence'

    _a					:  Union[str, Any]						=  tokenizer.encode(lowerCamelCase_      )
    _a					:  int						=  encoded.index(lowerCamelCase_      )
    _a					:  str						=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )

    _a					:  Optional[Any]						=  tokenizer.encode(lowerCamelCase_      )
    _a					:  int						=  encoded.index(lowerCamelCase_      )
    _a					:  Optional[Any]						=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
    self.assertNotEqual(lowerCamelCase_		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    pass




 def   __UpperCamelCase   (    self      )      ->    str:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
          _a					:  Optional[int]						=  self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		,      **lowerCamelCase_      )
          _a					:  Optional[int]						=  self.tokenizer_class.from_pretrained(lowerCamelCase_		,      **lowerCamelCase_      )
          _a					:  Any						=  'A, <mask> AllenNLP sentence.'
          _a					:  int						=  tokenizer_r.encode_plus(lowerCamelCase_		,      add_special_tokens=lowerCamelCase_		,      return_token_type_ids=lowerCamelCase_      )
          _a					:  List[Any]						=  tokenizer_p.encode_plus(lowerCamelCase_		,      add_special_tokens=lowerCamelCase_		,      return_token_type_ids=lowerCamelCase_      )

          # token_type_ids should put 0 everywhere
          self.assertEqual(sum(tokens_r['token_type_ids']      )		,      sum(tokens_p['token_type_ids']      )      )

          # attention_mask should put 1 everywhere, so sum over length should be 1
          self.assertEqual(
              sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )		,      sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )		,      )

          _a					:  Optional[int]						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
          _a					:  Union[str, Any]						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

          # Rust correctly handles the space before the mask while python doesnt
          self.assertSequenceEqual(tokens_p['input_ids']		,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
          self.assertSequenceEqual(tokens_r['input_ids']		,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

          self.assertSequenceEqual(
              lowerCamelCase_		,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
          self.assertSequenceEqual(
              lowerCamelCase_		,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    for trim_offsets, add_prefix_space in itertools.product([True, False]		,      repeat=2      ):
       _a					:  Optional[Any]						=  self.rust_tokenizer_class.from_pretrained(
           self.tmpdirname		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )

       _a					:  Union[str, Any]						=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
       _a					:  Dict						=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

       self.assertEqual(pre_tokenizer_state['add_prefix_space']		,      lowerCamelCase_      )

       self.assertEqual(post_processor_state['add_prefix_space']		,      lowerCamelCase_      )
       self.assertEqual(post_processor_state['trim_offsets']		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
    # `trim_offsets`
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
          _a					:  Dict						=  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
          _a					:  Optional[Any]						=  F'''{text_of_1_token} {text_of_1_token}'''

          _a					:  Dict						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Dict						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (len(lowerCamelCase_      ) + 1, len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  Any						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Optional[Any]						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (len(lowerCamelCase_      ) + 1, len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  List[str]						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Optional[Any]						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (len(lowerCamelCase_      ), len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  Tuple						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Optional[Any]						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (len(lowerCamelCase_      ), len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  List[str]						=  F''' {text}'''

          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
          # )
          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
          # self.assertEqual(
          #     encoding.offset_mapping[1],
          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
          # )

          _a					:  Optional[Any]						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Any						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (1, 1 + len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (1 + len(lowerCamelCase_      ) + 1, 1 + len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  List[Any]						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  Union[str, Any]						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, 1 + len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (1 + len(lowerCamelCase_      ), 1 + len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )

          _a					:  str						=  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_		,      use_fast=lowerCamelCase_		,      add_prefix_space=lowerCamelCase_		,      trim_offsets=lowerCamelCase_      )
          _a					:  int						=  tokenizer_r(lowerCamelCase_		,      return_offsets_mapping=lowerCamelCase_		,      add_special_tokens=lowerCamelCase_      )
          self.assertEqual(encoding.offset_mapping[0]		,      (0, 1 + len(lowerCamelCase_      ))      )
          self.assertEqual(
              encoding.offset_mapping[1]		,      (1 + len(lowerCamelCase_      ), 1 + len(lowerCamelCase_      ) + 1 + len(lowerCamelCase_      ))		,      )






'''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)


