



"""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 			snake_case_    ( ):


       '''simple docstring'''





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

       return image






def 			snake_case_    ( A_   :						Union[str, Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[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 			snake_case_    ( A_   :						str,							A_   :						Optional[Any],							A_   :						Dict		):


       '''simple docstring'''





       _lowerCamelCase		:							List[Any]            =					dct.pop(A_		)
       _lowerCamelCase		:							Dict            =					val






def 			snake_case_    ( A_   :						List[Any],							A_   :						List[Any]		):


       '''simple docstring'''





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

              # next, set bias in the state dict
              _lowerCamelCase		:							Union[str, Any]            =					torch.cat((q_bias, torch.zeros_like(A_,							requires_grad=A_		), v_bias)		)
              _lowerCamelCase		:							Any            =					qkv_bias






def 			snake_case_    ( A_   :						int		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					3_64 if '''coco''' in model_name else 2_24
       _lowerCamelCase		:							Optional[int]            =					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:
              _lowerCamelCase		:							List[str]            =					TaConfig.from_pretrained('''google/flan-t5-xl''',							dense_act_fn='''gelu''',							bos_token_id=1		).to_dict()
       elif "t5-xxl" in model_name:
              _lowerCamelCase		:							int            =					TaConfig.from_pretrained('''google/flan-t5-xxl''',							dense_act_fn='''gelu''',							bos_token_id=1		).to_dict()
       elif "vicuna-7b" in model_name:
              _lowerCamelCase		:							int            =					LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf''',							vocab_size=3_20_01		).to_dict()
       elif "vicuna-13b" in model_name:
              _lowerCamelCase		:							Dict            =					LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf''',							vocab_size=3_20_01		).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
       _lowerCamelCase		:							Any            =					InstructBlipQFormerConfig(vocab_size=3_05_23		).to_dict()
       _lowerCamelCase		:							List[Any]            =					InstructBlipConfig(vision_config=A_,							text_config=A_,							qformer_config=A_		)

       return config, image_size






@torch.no_grad()
def 			snake_case_    ( A_   :						str,							A_   :						Any=None,							A_   :						str=False		):


       '''simple docstring'''





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

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

       _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					get_blipa_config(A_		)
       _lowerCamelCase		:							Dict            =					InstructBlipForConditionalGeneration(A_		).eval()

       _lowerCamelCase		:							Optional[int]            =					{
           '''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'''),
       }

       _lowerCamelCase     ,       _lowerCamelCase		:							Any            =					model_name_to_original[model_name]

       # load original model
       print('''Loading original model...'''		)
       _lowerCamelCase		:							Tuple            =					'''cuda:1''' if torch.cuda.is_available() else '''cpu'''
       _lowerCamelCase		:							Dict            =					'''cuda:2''' if torch.cuda.is_available() else '''cpu'''
       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							str            =					load_model_and_preprocess(
           name=A_,							model_type=A_,							is_eval=A_,							device=A_		)
       original_model.eval()
       print('''Done!'''		)

       # update state dict keys
       _lowerCamelCase		:							Tuple            =					original_model.state_dict()
       _lowerCamelCase		:							Tuple            =					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():
              _lowerCamelCase		:							Tuple            =					state_dict.pop(A_		)
              if key.startswith('''Qformer.bert'''		):
                     _lowerCamelCase		:							str            =					key.replace('''Qformer.bert''',							'''qformer'''		)
              if "attention.self" in key:
                     _lowerCamelCase		:							List[Any]            =					key.replace('''self''',							'''attention'''		)
              if "llm_proj" in key:
                     _lowerCamelCase		:							Dict            =					key.replace('''llm_proj''',							'''language_projection'''		)
              if "t5_proj" in key:
                     _lowerCamelCase		:							List[Any]            =					key.replace('''t5_proj''',							'''language_projection'''		)
              if key.startswith('''llm_model'''		):
                     _lowerCamelCase		:							Optional[int]            =					key.replace('''llm_model''',							'''language_model'''		)
              if key.startswith('''t5'''		):
                     _lowerCamelCase		:							List[Any]            =					key.replace('''t5''',							'''language'''		)
              _lowerCamelCase		:							List[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_		)

       _lowerCamelCase		:							str            =					load_demo_image()
       _lowerCamelCase		:							Union[str, Any]            =					'''What is unusual about this image?'''

       # create processor
       _lowerCamelCase		:							Tuple            =					BlipImageProcessor(
           size={'''height''': image_size, '''width''': image_size},							image_mean=A_,							image_std=A_		)
       _lowerCamelCase		:							List[str]            =					InstructBlipProcessor(
           image_processor=A_,							tokenizer=A_,							qformer_tokenizer=A_,							)
       _lowerCamelCase		:							Dict            =					processor(images=A_,							text=A_,							return_tensors='''pt'''		).to(A_		)

       # make sure processor creates exact same pixel values
       _lowerCamelCase		:							Optional[Any]            =					vis_processors['''eval'''](A_		).unsqueeze(0		).to(A_		)
       _lowerCamelCase		:							List[Any]            =					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:
                     _lowerCamelCase		:							List[Any]            =					original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}		).logits
                     _lowerCamelCase		:							List[Any]            =					hf_model(**A_		).logits
              else:
                     _lowerCamelCase		:							Tuple            =					original_model(
                         {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}		).logits
                     _lowerCamelCase		:							str            =					tokenizer('''\n''',							return_tensors='''pt'''		).input_ids.to(A_		)
                     _lowerCamelCase		:							Optional[Any]            =					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,							-1_00		)
                     _lowerCamelCase		:							Any            =					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
       _lowerCamelCase		:							Tuple            =					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...'''		)
       _lowerCamelCase		:							str            =					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...'''		)
       _lowerCamelCase		:							Tuple            =					hf_model.generate(
           **A_,							do_sample=A_,							num_beams=5,							max_length=2_56,							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?
              _lowerCamelCase		:							Union[str, Any]            =					2
       print('''Original generation:''',							A_		)
       _lowerCamelCase		:							Dict            =					processor.batch_decode(A_,							skip_special_tokens=A_		)
       _lowerCamelCase		:							str            =					[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__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   lowerCAmelCase__              =							[
       '''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''',
   )

   lowerCAmelCase__              =							parser.parse_args()

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




import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


if __name__ == "__main__":
							print(F'''{solution() = }''')