


'''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 			_lowercase       (				):
 __A  :		List[str]       =			'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
 __A  :		List[str]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			).convert('RGB'			)

 return image


def 			_lowercase       (				UpperCamelCase__       :					Dict			):
 __A  :		List[Any]       =			[]
 # 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 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					int			):
 __A  :		Union[str, Any]       =			dct.pop(UpperCamelCase__			)
 __A  :		Dict       =			val


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any			):
 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  :		Optional[Any]       =			state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""			)

  # next, set bias in the state dict
  __A  :		List[Any]       =			torch.cat((q_bias, torch.zeros_like(UpperCamelCase__,				requires_grad=UpperCamelCase__			), v_bias)			)
  __A  :		Any       =			qkv_bias


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		Tuple       =			364 if 'coco' in model_name else 224
 __A  :		str       =			InstructBlipVisionConfig(image_size=UpperCamelCase__			).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  :		str       =			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  :		Optional[int]       =			LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf',				vocab_size=32001			).to_dict()
 elif "vicuna-13b" in model_name:
  __A  :		Union[str, Any]       =			LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf',				vocab_size=32001			).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  :		List[str]       =			InstructBlipQFormerConfig(vocab_size=30523			).to_dict()
 __A  :		int       =			InstructBlipConfig(vision_config=UpperCamelCase__,				text_config=UpperCamelCase__,				qformer_config=UpperCamelCase__			)

 return config, image_size


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False			):
 __A  :		Optional[int]       =			AutoTokenizer.from_pretrained('bert-base-uncased',				truncation_side='left'			)
 qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}			)

 if "t5" in model_name:
  __A  :		Optional[Any]       =			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  :		Union[str, 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  :		List[str]       =			get_blipa_config(UpperCamelCase__			)
 __A  :		Union[str, Any]       =			InstructBlipForConditionalGeneration(UpperCamelCase__			).eval()

 __A  :		List[str]       =			{
     '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  :		Any       =			model_name_to_original[model_name]

 # load original model
 print('Loading original model...'			)
 __A  :		List[Any]       =			'cuda:1' if torch.cuda.is_available() else 'cpu'
 __A  :		Union[str, Any]       =			'cuda:2' if torch.cuda.is_available() else 'cpu'
 __A     ,__A     ,__A  :		Any       =			load_model_and_preprocess(
     name=UpperCamelCase__,				model_type=UpperCamelCase__,				is_eval=UpperCamelCase__,				device=UpperCamelCase__			)
 original_model.eval()
 print('Done!'			)

 # update state dict keys
 __A  :		List[str]       =			original_model.state_dict()
 __A  :		Union[str, Any]       =			create_rename_keys(UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 # some keys can be renamed efficiently
 for key, val in state_dict.copy().items():
  __A  :		str       =			state_dict.pop(UpperCamelCase__			)
  if key.startswith('Qformer.bert'			):
   __A  :		Dict       =			key.replace('Qformer.bert',				'qformer'			)
  if "attention.self" in key:
   __A  :		Tuple       =			key.replace('self',				'attention'			)
  if "llm_proj" in key:
   __A  :		Tuple       =			key.replace('llm_proj',				'language_projection'			)
  if "t5_proj" in key:
   __A  :		Union[str, Any]       =			key.replace('t5_proj',				'language_projection'			)
  if key.startswith('llm_model'			):
   __A  :		str       =			key.replace('llm_model',				'language_model'			)
  if key.startswith('t5'			):
   __A  :		Optional[Any]       =			key.replace('t5',				'language'			)
  __A  :		str       =			val

 # read in qv biases
 read_in_q_v_bias(UpperCamelCase__,				UpperCamelCase__			)

 # note: weights get loaded in torch.float32 by default
 hf_model.load_state_dict(UpperCamelCase__,				strict=UpperCamelCase__			)

 __A  :		str       =			load_demo_image()
 __A  :		Any       =			'What is unusual about this image?'

 # create processor
 __A  :		List[str]       =			BlipImageProcessor(
     size={'height': image_size, 'width': image_size},				image_mean=UpperCamelCase__,				image_std=UpperCamelCase__			)
 __A  :		int       =			InstructBlipProcessor(
     image_processor=UpperCamelCase__,				tokenizer=UpperCamelCase__,				qformer_tokenizer=UpperCamelCase__,				)
 __A  :		Union[str, Any]       =			processor(images=UpperCamelCase__,				text=UpperCamelCase__,				return_tensors='pt'			).to(UpperCamelCase__			)

 # make sure processor creates exact same pixel values
 __A  :		Any       =			vis_processors['eval'](UpperCamelCase__			).unsqueeze(0			).to(UpperCamelCase__			)
 __A  :		List[str]       =			inputs.pixel_values
 assert torch.allclose(original_pixel_values.to(pixel_values.device			),				UpperCamelCase__			)

 original_model.to(UpperCamelCase__			)
 hf_model.to(UpperCamelCase__			)
 with torch.no_grad():
  if "vicuna" in model_name:
   __A  :		Tuple       =			original_model({'image': original_pixel_values, 'text_input': [prompt]}			).logits
   __A  :		Dict       =			hf_model(**UpperCamelCase__			).logits
  else:
   __A  :		int       =			original_model(
       {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}			).logits
   __A  :		Optional[int]       =			tokenizer('\n',				return_tensors='pt'			).input_ids.to(UpperCamelCase__			)
   __A  :		Optional[int]       =			label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,				-100			)
   __A  :		Optional[int]       =			hf_model(**UpperCamelCase__,				labels=UpperCamelCase__			).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  :		int       =			1E-4 if 'vicuna' in model_name else 1E-5
 assert torch.allclose(original_logits.to(logits.device			),				UpperCamelCase__,				atol=UpperCamelCase__			)
 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  :		List[Any]       =			hf_model.generate(
     **UpperCamelCase__,				do_sample=UpperCamelCase__,				num_beams=5,				max_length=256,				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  :		Union[str, Any]       =			2
 print('Original generation:',				UpperCamelCase__			)
 __A  :		List[str]       =			processor.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__			)
 __A  :		List[str]       =			[text.strip() for text in output_text]
 print('HF generation:',				UpperCamelCase__			)

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

 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_ :     List[str]								=      argparse.ArgumentParser()
 UpperCAmelCase_ :     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)










'''simple docstring'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






