





'''simple docstring'''






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class  _A :

		def __init__(   self					:   Tuple  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   int="resnet50"  ,      __magic_name__					:   Dict=3  ,      __magic_name__					:   Union[str, Any]=32  ,      __magic_name__					:   Union[str, Any]=3  ,      __magic_name__					:   Dict=True  ,      __magic_name__					:   List[str]=True  ,      )					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     parent
					__snake_case					:      List[Any]							      =     out_indices if out_indices is not None else [4]
					__snake_case					:      List[Any]							      =     stage_names
					__snake_case					:      Tuple							      =     out_features
					__snake_case					:      Any							      =     backbone
					__snake_case					:      List[str]							      =     batch_size
					__snake_case					:      int							      =     image_size
					__snake_case					:      Tuple							      =     num_channels
					__snake_case					:      Optional[int]							      =     use_pretrained_backbone
					__snake_case					:      Any							      =     is_training

		def 	lowercase__     (   self					:   Tuple			)					->    str:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
					__snake_case					:      List[Any]							      =     self.get_config()

					return config, pixel_values

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					return TimmBackboneConfig(
					    image_size=self.image_size  ,      num_channels=self.num_channels  ,      out_features=self.out_features  ,      out_indices=self.out_indices  ,      stage_names=self.stage_names  ,      use_pretrained_backbone=self.use_pretrained_backbone  ,      backbone=self.backbone  ,      )

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     TimmBackbone(config=__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					with torch.no_grad():
								__snake_case					:      Union[str, Any]							      =     model(__magic_name__			)
					self.parent.assertEqual(
					    result.feature_map[-1].shape  ,      (self.batch_size, model.channels[-1], 14, 14)  ,      )

		def 	lowercase__     (   self					:   str			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      int							      =     self.prepare_config_and_inputs()
					__snake_case      ,     __snake_case					:      Dict							      =     config_and_inputs
					__snake_case					:      Any							      =     {"""pixel_values""": pixel_values}
					return config, inputs_dict





@require_torch
@require_timm
class  _A (					__lowercase						,					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   str				     =   (TimmBackbone,) if is_torch_available() else ()
		lowercase__:   Optional[int]				     =   {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
		lowercase__:   Tuple				     =   False
		lowercase__:   List[str]				     =   False
		lowercase__:   Any				     =   False
		lowercase__:   Optional[int]				     =   False

		def 	lowercase__     (   self					:   Dict			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Dict							      =     TimmBackboneModelTester(self			)
					__snake_case					:      Tuple							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      has_text_modality=__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    str:


					"""simple docstring"""

					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

		def 	lowercase__     (   self					:   Optional[int]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     """resnet18"""
					__snake_case					:      Union[str, Any]							      =     """microsoft/resnet-18"""

					__snake_case					:      Union[str, Any]							      =     AutoBackbone.from_pretrained(__magic_name__  ,      use_timm_backbone=__magic_name__			)
					__snake_case					:      Any							      =     AutoBackbone.from_pretrained(__magic_name__			)

					self.assertEqual(len(timm_model.out_features			)  ,      len(transformers_model.out_features			)			)
					self.assertEqual(len(timm_model.stage_names			)  ,      len(transformers_model.stage_names			)			)
					self.assertEqual(timm_model.channels  ,      transformers_model.channels			)
					# Out indices are set to the last layer by default. For timm models, we don't know
					# the number of layers in advance, so we set it to (-1,), whereas for transformers
					# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
					self.assertEqual(timm_model.out_indices  ,      (-1,)			)
					self.assertEqual(transformers_model.out_indices  ,      [len(timm_model.stage_names			) - 1]			)

					__snake_case					:      Union[str, Any]							      =     AutoBackbone.from_pretrained(__magic_name__  ,      use_timm_backbone=__magic_name__  ,      out_indices=[1, 2, 3]			)
					__snake_case					:      Tuple							      =     AutoBackbone.from_pretrained(__magic_name__  ,      out_indices=[1, 2, 3]			)

					self.assertEqual(timm_model.out_indices  ,      transformers_model.out_indices			)
					self.assertEqual(len(timm_model.out_features			)  ,      len(transformers_model.out_features			)			)
					self.assertEqual(timm_model.channels  ,      transformers_model.channels			)

		@unittest.skip("""TimmBackbone doesn't support feed forward chunking"""			)
		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""			)
		def 	lowercase__     (   self					:   str			)					->    List[str]:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone initialization is managed on the timm side"""			)
		def 	lowercase__     (   self					:   Any			)					->    Optional[int]:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""			)
		def 	lowercase__     (   self					:   List[Any]			)					->    Any:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""			)
		def 	lowercase__     (   self					:   Dict			)					->    int:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""			)
		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""			)
		def 	lowercase__     (   self					:   Tuple			)					->    List[str]:


					"""simple docstring"""

					pass

		@unittest.skip("""model weights aren't tied in TimmBackbone."""			)
		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					pass

		@unittest.skip("""model weights aren't tied in TimmBackbone."""			)
		def 	lowercase__     (   self					:   Dict			)					->    int:


					"""simple docstring"""

					pass

		@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""			)
		def 	lowercase__     (   self					:   Dict			)					->    List[Any]:


					"""simple docstring"""

					pass

		@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""			)
		def 	lowercase__     (   self					:   Any			)					->    List[str]:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""			)
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					pass

		@unittest.skip("""TimmBackbone doesn't support output_attentions."""			)
		def 	lowercase__     (   self					:   List[str]			)					->    List[Any]:


					"""simple docstring"""

					pass

		@unittest.skip("""Safetensors is not supported by timm."""			)
		def 	lowercase__     (   self					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					pass

		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
		def 	lowercase__     (   self					:   int			)					->    Dict:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Dict:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      Optional[Any]							      =     model_class(__magic_name__			)
								__snake_case					:      Union[str, Any]							      =     inspect.signature(model.forward			)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__snake_case					:      str							      =     [*signature.parameters.keys()]

								__snake_case					:      int							      =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]  ,      __magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case					:      Tuple							      =     True
					__snake_case					:      List[Any]							      =     self.has_attentions

					# no need to test all models as different heads yield the same functionality
					__snake_case					:      Tuple							      =     self.all_model_classes[0]
					__snake_case					:      Dict							      =     model_class(__magic_name__			)
					model.to(__magic_name__			)

					__snake_case					:      str							      =     self._prepare_for_class(__magic_name__  ,      __magic_name__			)
					__snake_case					:      Optional[int]							      =     model(**__magic_name__			)
					__snake_case					:      Optional[Any]							      =     outputs[0][-1]

					# Encoder-/Decoder-only models
					__snake_case					:      Union[str, Any]							      =     outputs.hidden_states[0]
					hidden_states.retain_grad()

					if self.has_attentions:
								__snake_case					:      int							      =     outputs.attentions[0]
								attentions.retain_grad()

					output.flatten()[0].backward(retain_graph=__magic_name__			)

					self.assertIsNotNone(hidden_states.grad			)

					if self.has_attentions:
								self.assertIsNotNone(attentions.grad			)

		def 	lowercase__     (   self					:   List[str]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[int]							      =     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__snake_case					:      Optional[int]							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								__snake_case					:      Any							      =     model(**__magic_name__			)

								self.assertEqual(len(result.feature_maps			)  ,      len(config.out_indices			)			)
								self.assertEqual(len(model.channels			)  ,      len(config.out_indices			)			)

								# Check output of last stage is taken if out_features=None, out_indices=None
								__snake_case					:      Dict							      =     copy.deepcopy(__magic_name__			)
								__snake_case					:      Optional[Any]							      =     None
								__snake_case					:      int							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								__snake_case					:      int							      =     model(**__magic_name__			)

								self.assertEqual(len(result.feature_maps			)  ,      1			)
								self.assertEqual(len(model.channels			)  ,      1			)

								# Check backbone can be initialized with fresh weights
								__snake_case					:      Union[str, Any]							      =     copy.deepcopy(__magic_name__			)
								__snake_case					:      Optional[int]							      =     False
								__snake_case					:      str							      =     model_class(__magic_name__			)
								model.to(__magic_name__			)
								model.eval()
								__snake_case					:      int							      =     model(**__magic_name__			)







"""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
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 		__snake_case		( )    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							).convert("RGB"							)

     return image

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      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.layernorm.weight")							)
     rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")							)

     # fmt: on
     return rename_keys

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       dct.pop(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       364 if "coco" in model_name else 224
     _UpperCAmelCase     :      Dict														=       BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE__							).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     :      Any														=       OPTConfig.from_pretrained("facebook/opt-2.7b"		,    eos_token_id=SCREAMING_SNAKE_CASE__							).to_dict()
     elif "opt-6.7b" in model_name:
          _UpperCAmelCase     :      List[Any]														=       OPTConfig.from_pretrained("facebook/opt-6.7b"		,    eos_token_id=SCREAMING_SNAKE_CASE__							).to_dict()
     elif "t5-xl" in model_name:
          _UpperCAmelCase     :      Tuple														=       TaConfig.from_pretrained("google/flan-t5-xl"		,    dense_act_fn="gelu"		,    bos_token_id=1							).to_dict()
     elif "t5-xxl" in model_name:
          _UpperCAmelCase     :      int														=       TaConfig.from_pretrained("google/flan-t5-xxl"		,    dense_act_fn="gelu"		,    bos_token_id=1							).to_dict()

     _UpperCAmelCase     :      Optional[Any]														=       BlipaConfig(vision_config=SCREAMING_SNAKE_CASE__		,    text_config=SCREAMING_SNAKE_CASE__							)

     return config, image_size

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]=False							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       (
         AutoTokenizer.from_pretrained("facebook/opt-2.7b"							)
         if "opt" in model_name
         else AutoTokenizer.from_pretrained("google/flan-t5-xl"							)
     )
     _UpperCAmelCase     :      Optional[int]														=       tokenizer("\n"		,    add_special_tokens=SCREAMING_SNAKE_CASE__							).input_ids[0]
     _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       get_blipa_config(SCREAMING_SNAKE_CASE__		,    eos_token_id=SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE__							).eval()

     _UpperCAmelCase     :      Optional[int]														=       {
         "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     :      str														=       model_name_to_original[model_name]

     # load original model
     print("Loading original model..."							)
     _UpperCAmelCase     :      List[Any]														=       "cuda" if torch.cuda.is_available() else "cpu"
     _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       load_model_and_preprocess(
         name=SCREAMING_SNAKE_CASE__		,    model_type=SCREAMING_SNAKE_CASE__		,    is_eval=SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     original_model.eval()
     print("Done!"							)

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

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          _UpperCAmelCase     :      Tuple														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          if key.startswith("Qformer.bert"							):
               _UpperCAmelCase     :      List[Any]														=       key.replace("Qformer.bert"		,    "qformer"							)
          if "attention.self" in key:
               _UpperCAmelCase     :      Dict														=       key.replace("self"		,    "attention"							)
          if "opt_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       key.replace("opt_proj"		,    "language_projection"							)
          if "t5_proj" in key:
               _UpperCAmelCase     :      Optional[Any]														=       key.replace("t5_proj"		,    "language_projection"							)
          if key.startswith("opt"							):
               _UpperCAmelCase     :      Dict														=       key.replace("opt"		,    "language"							)
          if key.startswith("t5"							):
               _UpperCAmelCase     :      Union[str, Any]														=       key.replace("t5"		,    "language"							)
          _UpperCAmelCase     :      List[str]														=       val

     # read in qv biases
     read_in_q_v_bias(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       hf_model.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)
     assert len(SCREAMING_SNAKE_CASE__							) == 0
     assert unexpected_keys == ["qformer.embeddings.position_ids"]

     _UpperCAmelCase     :      Dict														=       load_demo_image()
     _UpperCAmelCase     :      List[Any]														=       vis_processors["eval"](SCREAMING_SNAKE_CASE__							).unsqueeze(0							).to(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Optional[Any]														=       tokenizer(["\n"]		,    return_tensors="pt"							).input_ids.to(SCREAMING_SNAKE_CASE__							)

     # create processor
     _UpperCAmelCase     :      Any														=       BlipImageProcessor(
         size={"height": image_size, "width": image_size}		,    image_mean=SCREAMING_SNAKE_CASE__		,    image_std=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Dict														=       BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Dict														=       processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							).pixel_values.to(SCREAMING_SNAKE_CASE__							)

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

     original_model.to(SCREAMING_SNAKE_CASE__							)
     hf_model.to(SCREAMING_SNAKE_CASE__							)
     with torch.no_grad():
          if "opt" in model_name:
               _UpperCAmelCase     :      str														=       original_model({"image": original_pixel_values, "text_input": [""]}							).logits
               _UpperCAmelCase     :      Optional[int]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							).logits
          else:
               _UpperCAmelCase     :      List[Any]														=       original_model(
                   {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}							).logits
               _UpperCAmelCase     :      List[str]														=       input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,    -100							)
               _UpperCAmelCase     :      Union[str, Any]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    labels=SCREAMING_SNAKE_CASE__							).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     :      List[str]														=       torch.tensor(
              [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]		,    device=SCREAMING_SNAKE_CASE__							)
          assert torch.allclose(logits[0, :3, :3]		,    SCREAMING_SNAKE_CASE__		,    atol=1E-4							)
     elif model_name == "blip2-flan-t5-xl-coco":
          _UpperCAmelCase     :      Any														=       torch.tensor(
              [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]		,    device=SCREAMING_SNAKE_CASE__							)
     else:
          # cast to same type
          _UpperCAmelCase     :      List[str]														=       logits.dtype
          assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE__							)		,    SCREAMING_SNAKE_CASE__		,    atol=1E-2							)
     print("Looks ok!"							)

     print("Generating a caption..."							)
     _UpperCAmelCase     :      int														=       ""
     _UpperCAmelCase     :      Any														=       tokenizer(SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							).input_ids.to(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      List[Any]														=       original_model.generate({"image": original_pixel_values}							)
     _UpperCAmelCase     :      int														=       hf_model.generate(
         SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    do_sample=SCREAMING_SNAKE_CASE__		,    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:"		,    SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       input_ids.shape[1]
     _UpperCAmelCase     :      List[Any]														=       processor.batch_decode(outputs[:, prompt_length:]		,    skip_special_tokens=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       [text.strip() for text in output_text]
     print("HF generation:"		,    SCREAMING_SNAKE_CASE__							)

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

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


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 _lowerCAmelCase							:   List[Any]				=				[
     "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",
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()

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