
import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def 				A	( lowercase			)       ->  Tuple:
		'''simple docstring'''

		if model.config.model_type == "gpt2":
				return model.transformer.h[0].mlp.c_fc
		return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
					import torch
					import torch.nn as nn


					class     lowercase (		nn.Module       ):



							def __init__(			self    ,				A_    ,				A_	)  -> List[str]:
									"""simple docstring"""
									super().__init__()
									UpperCamelCase          =					module
									UpperCamelCase          =					nn.Sequential(
									    nn.Linear(module.in_features    ,				A_    ,				bias=A_	)    ,				nn.Linear(A_    ,				module.out_features    ,				bias=A_	)    ,				)
									UpperCamelCase          =					(2.0 / (5 * min(module.in_features    ,				module.out_features	))) ** 0.5
									nn.init.normal_(self.adapter[0].weight    ,				std=A_	)
									nn.init.zeros_(self.adapter[1].weight	)
									self.adapter.to(module.weight.device	)



							def __UpperCamelCase			(			self    ,				A_    ,				*A_    ,				**A_	)  -> Dict:
									"""simple docstring"""
									return self.module(A_    ,				*A_    ,				**A_	) + self.adapter(A_	)


@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     lowercase (		unittest.TestCase       ):
		# We keep the constants inside the init function and model loading inside setUp function

		# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
		# Therefore here we use only bloom-1b3 to test our module
		__lowercase	:	List[str]     					=    "bigscience/bloom-1b7"

		# Constant values
		__lowercase	:	List[str]     					=    2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

		__lowercase	:	List[Any]     					=    "Hello my name is"
		__lowercase	:	str     					=    set()
		EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"       )
		EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"       )
		EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"       )
		__lowercase	:	Tuple     					=    10



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				# Models and tokenizer
				UpperCamelCase          =					AutoTokenizer.from_pretrained(self.model_name	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Models and tokenizer
				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(
				    self.model_name    ,				torch_dtype=torch.floataa    ,				device_map='auto'	)
				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				del self.model_fpaa
				del self.model_abit

				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.model_abit.config

				self.assertTrue(hasattr(A_    ,				'quantization_config'	)	)

				UpperCamelCase          =					config.to_dict()
				UpperCamelCase          =					config.to_diff_dict()

				UpperCamelCase          =					config.to_json_string()



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				from bitsandbytes.nn import Paramsabit

				UpperCamelCase          =					self.model_fpaa.get_memory_footprint()
				UpperCamelCase          =					self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit    ,				self.EXPECTED_RELATIVE_DIFFERENCE	)
				UpperCamelCase          =					get_some_linear_layer(self.model_abit	)
				self.assertTrue(linear.weight.__class__ == Paramsabit	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				from transformers import TaPreTrainedModel

				self.model_fpaa.get_memory_footprint()
				self.model_abit.get_memory_footprint()

				for name, module in self.model_abit.named_modules():
						if isinstance(A_    ,				torch.nn.Linear	):
								if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
										# 4-bit parameters are packed in uint8 variables
										self.assertTrue(module.weight.dtype == torch.uinta	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	)
				UpperCamelCase          =					self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0	)    ,				max_new_tokens=10	)

				self.assertIn(self.tokenizer.decode(output_sequences[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					BitsAndBytesConfig()
				UpperCamelCase          =					True

				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(
				    self.model_name    ,				quantization_config=A_    ,				device_map='auto'	)

				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	)
				UpperCamelCase          =					model_abit_from_config.generate(
				    input_ids=encoded_input['input_ids'].to(0	)    ,				max_new_tokens=10	)

				self.assertIn(self.tokenizer.decode(output_sequences[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				with self.assertRaises(A_	), tempfile.TemporaryDirectory() as tmpdirname:
						self.model_abit.save_pretrained(A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					BitsAndBytesConfig()

				with self.assertRaises(A_	):
						UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(
						    self.model_name    ,				quantization_config=A_    ,				load_in_abit=A_    ,				device_map='auto'    ,				bnb_abit_quant_type='nf4'    ,				)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				with self.assertRaises(A_	):
						# Tries with `str`
						self.model_abit.to('cpu'	)

				with self.assertRaises(A_	):
						# Tries with a `dtype``
						self.model_abit.to(torch.floataa	)

				with self.assertRaises(A_	):
						# Tries with a `device`
						self.model_abit.to(torch.device('cuda:0'	)	)

				with self.assertRaises(A_	):
						# Tries with a `device`
						self.model_abit.float()

				with self.assertRaises(A_	):
						# Tries with a `device`
						self.model_abit.half()

				# Test if we did not break anything
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	)

				UpperCamelCase          =					self.model_fpaa.to(torch.floataa	)
				UpperCamelCase          =					self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0	)    ,				max_new_tokens=10	)

				# Check this does not throw an error
				UpperCamelCase          =					self.model_fpaa.to('cpu'	)

				# Check this does not throw an error
				UpperCamelCase          =					self.model_fpaa.half()

				# Check this does not throw an error
				UpperCamelCase          =					self.model_fpaa.float()



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					AutoModelForSeqaSeqLM.from_pretrained('t5-small'    ,				load_in_abit=A_    ,				device_map='auto'	)
				self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa	)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     lowercase (		unittest.TestCase       ):



		@classmethod
		def __UpperCamelCase			(			cls	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					't5-small'
				UpperCamelCase          =					'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
				UpperCamelCase          =					AutoTokenizer.from_pretrained(cls.model_name	)
				UpperCamelCase          =					'Translate in German: Hello, my dog is cute'



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				from transformers import TaForConditionalGeneration

				UpperCamelCase          =					TaForConditionalGeneration._keep_in_fpaa_modules
				UpperCamelCase          =					None

				# test with `t5-small`
				UpperCamelCase          =					TaForConditionalGeneration.from_pretrained(self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	).to(0	)
				UpperCamelCase          =					model.generate(**A_	)

				# test with `flan-t5-small`
				UpperCamelCase          =					TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	).to(0	)
				UpperCamelCase          =					model.generate(**A_	)
				UpperCamelCase          =					modules



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				UpperCamelCase          =					TaForConditionalGeneration.from_pretrained(self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)

				# there was a bug with decoders - this test checks that it is fixed
				self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q    ,				bnb.nn.Linearabit	)	)

				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	).to(0	)
				UpperCamelCase          =					model.generate(**A_	)

				# test with `flan-t5-small`
				UpperCamelCase          =					TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	).to(0	)
				UpperCamelCase          =					model.generate(**A_	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()
				# model_name
				UpperCamelCase          =					'bigscience/bloom-560m'
				UpperCamelCase          =					't5-small'

				# Different types of model

				UpperCamelCase          =					AutoModel.from_pretrained(self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				# Sequence classification model
				UpperCamelCase          =					AutoModelForSequenceClassification.from_pretrained(
				    self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				# CausalLM model
				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(self.model_name    ,				load_in_abit=A_    ,				device_map='auto'	)
				# Seq2seq model
				UpperCamelCase          =					AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name    ,				load_in_abit=A_    ,				device_map='auto'	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				del self.base_model
				del self.sequence_model
				del self.model_abit
				del self.seq_to_seq_model

				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				from bitsandbytes.nn import Paramsabit

				self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit	)

				# Other heads should be nn.Parameter
				self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter	)
				self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter	)
				self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				del self.pipe

				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					pipeline(
				    'text-generation'    ,				model=self.model_name    ,				model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa}    ,				max_new_tokens=self.MAX_NEW_TOKENS    ,				)

				# Real second forward pass
				UpperCamelCase          =					self.pipe(self.input_text	)
				self.assertIn(pipeline_output[0]['generated_text']    ,				self.EXPECTED_OUTPUTS	)






@require_torch_multi_gpu
class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				super().setUp()



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(
				    self.model_name    ,				load_in_abit=A_    ,				device_map='balanced'	)

				# Check correct device map
				self.assertEqual(set(model_parallel.hf_device_map.values()	)    ,				{0, 1}	)

				# Check that inference pass works on the model
				UpperCamelCase          =					self.tokenizer(self.input_text    ,				return_tensors='pt'	)

				# Second real batch
				UpperCamelCase          =					model_parallel.generate(input_ids=encoded_input['input_ids'].to(0	)    ,				max_new_tokens=10	)
				self.assertIn(self.tokenizer.decode(output_parallel[0]    ,				skip_special_tokens=A_	)    ,				self.EXPECTED_OUTPUTS	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					'facebook/opt-350m'
				super().setUp()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				if version.parse(importlib.metadata.version('bitsandbytes'	)	) < version.parse('0.37.0'	):
						return

				# Step 1: freeze all parameters
				UpperCamelCase          =					AutoModelForCausalLM.from_pretrained(self.model_name    ,				load_in_abit=A_	)

				self.assertEqual(set(model.hf_device_map.values()	)    ,				{torch.cuda.current_device()}	)

				for param in model.parameters():
						UpperCamelCase          =					False  # freeze the model - train adapters later
						if param.ndim == 1:
								# cast the small parameters (e.g. layernorm) to fp32 for stability
								UpperCamelCase          =					param.data.to(torch.floataa	)

        # Step 2: add adapters
				for _, module in model.named_modules():
						if "OPTAttention" in repr(type(A_	)	):
								UpperCamelCase          =					LoRALayer(module.q_proj    ,				rank=16	)
								UpperCamelCase          =					LoRALayer(module.k_proj    ,				rank=16	)
								UpperCamelCase          =					LoRALayer(module.v_proj    ,				rank=16	)

        # Step 3: dummy batch
				UpperCamelCase          =					self.tokenizer('Test batch '    ,				return_tensors='pt'	).to(0	)

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
						UpperCamelCase          =					model.forward(**A_	)
						out.logits.norm().backward()

				for module in model.modules():
						if isinstance(A_    ,				A_	):
								self.assertTrue(module.adapter[1].weight.grad is not None	)
								self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0	)
						elif isinstance(A_    ,				nn.Embedding	):
								self.assertTrue(module.weight.grad is None	)






class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Dict     					=    "gpt2-xl"
		__lowercase	:	int     					=    3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7


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
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 				A	( )       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			).convert('RGB'			)

		return image


def 				A	( lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# 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.layernorm.weight')			)
		rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')			)

		# fmt: on
		return rename_keys


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Dict:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase			)       ->  List[str]:
		'''simple docstring'''

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

				# next, set bias in the state dict
				UpperCamelCase          =					torch.cat((q_bias, torch.zeros_like(lowercase   ,	requires_grad=lowercase			), v_bias)			)
				UpperCamelCase          =					qkv_bias


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					364 if 'coco' in model_name else 224
		UpperCamelCase          =					BlipaVisionConfig(image_size=lowercase			).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 "opt-2.7b" in model_name:
				UpperCamelCase          =					OPTConfig.from_pretrained('facebook/opt-2.7b'   ,	eos_token_id=lowercase			).to_dict()
		elif "opt-6.7b" in model_name:
				UpperCamelCase          =					OPTConfig.from_pretrained('facebook/opt-6.7b'   ,	eos_token_id=lowercase			).to_dict()
		elif "t5-xl" in model_name:
				UpperCamelCase          =					TaConfig.from_pretrained('google/flan-t5-xl'   ,	dense_act_fn='gelu'   ,	bos_token_id=1			).to_dict()
		elif "t5-xxl" in model_name:
				UpperCamelCase          =					TaConfig.from_pretrained('google/flan-t5-xxl'   ,	dense_act_fn='gelu'   ,	bos_token_id=1			).to_dict()

		UpperCamelCase          =					BlipaConfig(vision_config=lowercase   ,	text_config=lowercase			)

		return config, image_size


@torch.no_grad()
def 				A	( lowercase   ,	lowercase=None   ,	lowercase=False			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					(
		    AutoTokenizer.from_pretrained('facebook/opt-2.7b'			)
		    if 'opt' in model_name
		    else AutoTokenizer.from_pretrained('google/flan-t5-xl'			)
		)
		UpperCamelCase          =					tokenizer('\n'   ,	add_special_tokens=lowercase			).input_ids[0]
		UpperCamelCase     ,  UpperCamelCase          =					get_blipa_config(lowercase   ,	eos_token_id=lowercase			)

		UpperCamelCase          =					BlipaForConditionalGeneration(lowercase			).eval()

		UpperCamelCase          =					{
		    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
		    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
		    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
		    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
		    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
		    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
		    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
		}

		UpperCamelCase     ,  UpperCamelCase          =					model_name_to_original[model_name]

		# load original model
		print('Loading original model...'			)
		UpperCamelCase          =					'cuda' if torch.cuda.is_available() else 'cpu'
		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					load_model_and_preprocess(
		    name=lowercase   ,	model_type=lowercase   ,	is_eval=lowercase   ,	device=lowercase			)
		original_model.eval()
		print('Done!'			)

		# update state dict keys
		UpperCamelCase          =					original_model.state_dict()
		UpperCamelCase          =					create_rename_keys(lowercase			)
		for src, dest in rename_keys:
				rename_key(lowercase   ,	lowercase   ,	lowercase			)

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				UpperCamelCase          =					state_dict.pop(lowercase			)
				if key.startswith('Qformer.bert'			):
						UpperCamelCase          =					key.replace('Qformer.bert'   ,	'qformer'			)
				if "attention.self" in key:
						UpperCamelCase          =					key.replace('self'   ,	'attention'			)
				if "opt_proj" in key:
						UpperCamelCase          =					key.replace('opt_proj'   ,	'language_projection'			)
				if "t5_proj" in key:
						UpperCamelCase          =					key.replace('t5_proj'   ,	'language_projection'			)
				if key.startswith('opt'			):
						UpperCamelCase          =					key.replace('opt'   ,	'language'			)
				if key.startswith('t5'			):
						UpperCamelCase          =					key.replace('t5'   ,	'language'			)
				UpperCamelCase          =					val

		# read in qv biases
		read_in_q_v_bias(lowercase   ,	lowercase			)

		UpperCamelCase     ,  UpperCamelCase          =					hf_model.load_state_dict(lowercase   ,	strict=lowercase			)
		assert len(lowercase			) == 0
		assert unexpected_keys == ["qformer.embeddings.position_ids"]

		UpperCamelCase          =					load_demo_image()
		UpperCamelCase          =					vis_processors['eval'](lowercase			).unsqueeze(0			).to(lowercase			)
		UpperCamelCase          =					tokenizer(['\n']   ,	return_tensors='pt'			).input_ids.to(lowercase			)

		# create processor
		UpperCamelCase          =					BlipImageProcessor(
		    size={'height': image_size, 'width': image_size}   ,	image_mean=lowercase   ,	image_std=lowercase			)
		UpperCamelCase          =					BlipaProcessor(image_processor=lowercase   ,	tokenizer=lowercase			)
		UpperCamelCase          =					processor(images=lowercase   ,	return_tensors='pt'			).pixel_values.to(lowercase			)

		# make sure processor creates exact same pixel values
		assert torch.allclose(lowercase   ,	lowercase			)

		original_model.to(lowercase			)
		hf_model.to(lowercase			)
		with torch.no_grad():
				if "opt" in model_name:
						UpperCamelCase          =					original_model({'image': original_pixel_values, 'text_input': ['']}			).logits
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase			).logits
				else:
						UpperCamelCase          =					original_model(
						    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}			).logits
						UpperCamelCase          =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id   ,	-100			)
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase   ,	labels=lowercase			).logits

		assert original_logits.shape == logits.shape
		print('First values of original logits:'   ,	original_logits[0, :3, :3]			)
		print('First values of HF logits:'   ,	logits[0, :3, :3]			)

		# assert values
		if model_name == "blip2-flan-t5-xl":
				UpperCamelCase          =					torch.tensor(
				    [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]   ,	device=lowercase			)
				assert torch.allclose(logits[0, :3, :3]   ,	lowercase   ,	atol=1e-4			)
		elif model_name == "blip2-flan-t5-xl-coco":
				UpperCamelCase          =					torch.tensor(
				    [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]   ,	device=lowercase			)
		else:
				# cast to same type
				UpperCamelCase          =					logits.dtype
				assert torch.allclose(original_logits.to(lowercase			)   ,	lowercase   ,	atol=1e-2			)
		print('Looks ok!'			)

		print('Generating a caption...'			)
		UpperCamelCase          =					''
		UpperCamelCase          =					tokenizer(lowercase   ,	return_tensors='pt'			).input_ids.to(lowercase			)

		UpperCamelCase          =					original_model.generate({'image': original_pixel_values}			)
		UpperCamelCase          =					hf_model.generate(
		    lowercase   ,	lowercase   ,	do_sample=lowercase   ,	num_beams=5   ,	max_length=30   ,	min_length=1   ,	top_p=0.9   ,	repetition_penalty=1.0   ,	length_penalty=1.0   ,	temperature=1   ,	)
		print('Original generation:'   ,	lowercase			)
		UpperCamelCase          =					input_ids.shape[1]
		UpperCamelCase          =					processor.batch_decode(outputs[:, prompt_length:]   ,	skip_special_tokens=lowercase			)
		UpperCamelCase          =					[text.strip() for text in output_text]
		print('HF generation:'   ,	lowercase			)

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

		if push_to_hub:
				processor.push_to_hub(f'''nielsr/{model_name}'''			)
				hf_model.push_to_hub(f'''nielsr/{model_name}'''			)


if __name__ == "__main__":
					_UpperCAmelCase :			Optional[int]              =	argparse.ArgumentParser()
					_UpperCAmelCase :			str              =	[
					    "blip2-opt-2.7b",
					    "blip2-opt-6.7b",
					    "blip2-opt-2.7b-coco",
					    "blip2-opt-6.7b-coco",
					    "blip2-flan-t5-xl",
					    "blip2-flan-t5-xl-coco",
					    "blip2-flan-t5-xxl",
					]
					parser.add_argument(
					    "--model_name",
					    default="blip2-opt-2.7b",
					    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 :			List[str]              =	parser.parse_args()

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