




"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor






class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def __init__(       self:	Tuple,   a_:	Optional[Any],   a_:	Optional[Any]=7,   a_:	Any=3,   a_:	Tuple=18,   a_:	int=30,   a_:	Union[str, Any]=400,   a_:	List[Any]=True,   a_:	int=None,   a_:	Optional[Any]=True,   a_:	List[str]=None,   a_:	Optional[Any]=True,   a_:	Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073],   a_:	Optional[int]=[0.26_862_954, 0.26_130_258, 0.27_577_711],   a_:	Union[str, Any]=True,   ):




   '''simple docstring'''



   _snake_case							:	str			=						size if size is not None else {"""height""": 224, """width""": 224}
   _snake_case							:	str			=						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   _snake_case							:	Tuple			=						parent
   _snake_case							:	List[str]			=						batch_size
   _snake_case							:	Tuple			=						num_channels
   _snake_case							:	int			=						image_size
   _snake_case							:	Dict			=						min_resolution
   _snake_case							:	Optional[Any]			=						max_resolution
   _snake_case							:	Any			=						do_resize
   _snake_case							:	Tuple			=						size
   _snake_case							:	int			=						do_center_crop
   _snake_case							:	List[Any]			=						crop_size
   _snake_case							:	Optional[Any]			=						do_normalize
   _snake_case							:	Optional[Any]			=						image_mean
   _snake_case							:	Dict			=						image_std
   _snake_case							:	str			=						do_convert_rgb


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_convert_rgb": self.do_convert_rgb,
   }


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Any=False,   a_:	List[str]=False,   a_:	Union[str, Any]=False	):




   '''simple docstring'''



   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   if equal_resolution:
     _snake_case							:	List[Any]			=						[]
     for i in range(self.batch_size	):
       image_inputs.append(
           np.random.randint(
               255,   size=(self.num_channels, self.max_resolution, self.max_resolution),   dtype=np.uinta	)	)
   else:
     _snake_case							:	Any			=						[]
     for i in range(self.batch_size	):
       _snake_case	,      _snake_case							:	Union[str, Any]			=						np.random.choice(np.arange(self.min_resolution,   self.max_resolution	),   2	)
       image_inputs.append(np.random.randint(255,   size=(self.num_channels, width, height),   dtype=np.uinta	)	)

   if not numpify and not torchify:
     # PIL expects the channel dimension as last dimension
     _snake_case							:	str			=						[Image.fromarray(np.moveaxis(a_,   0,   -1	)	) for x in image_inputs]

   if torchify:
     _snake_case							:	Dict			=						[torch.from_numpy(a_	) for x in image_inputs]

   return image_inputs







@require_torch
@require_vision
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ChineseCLIPImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						ChineseCLIPImageProcessingTester(self,   do_center_crop=a_	)


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.image_processing_class(**self.image_processor_dict	)
   self.assertTrue(hasattr(a_,   """do_resize"""	)	)
   self.assertTrue(hasattr(a_,   """size"""	)	)
   self.assertTrue(hasattr(a_,   """do_center_crop"""	)	)
   self.assertTrue(hasattr(a_,   """center_crop"""	)	)
   self.assertTrue(hasattr(a_,   """do_normalize"""	)	)
   self.assertTrue(hasattr(a_,   """image_mean"""	)	)
   self.assertTrue(hasattr(a_,   """image_std"""	)	)
   self.assertTrue(hasattr(a_,   """do_convert_rgb"""	)	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.image_processing_class.from_dict(self.image_processor_dict	)
   self.assertEqual(image_processor.size,   {"""height""": 224, """width""": 224}	)
   self.assertEqual(image_processor.crop_size,   {"""height""": 18, """width""": 18}	)

   _snake_case							:	Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict,   size=42,   crop_size=84	)
   self.assertEqual(image_processor.size,   {"""shortest_edge""": 42}	)
   self.assertEqual(image_processor.crop_size,   {"""height""": 84, """width""": 84}	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PIL images
   _snake_case							:	Any			=						self.image_processor_tester.prepare_inputs(equal_resolution=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   Image.Image	)

   # Test not batched input
   _snake_case							:	int			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )

   # Test batched
   _snake_case							:	Union[str, Any]			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processing_class(**self.image_processor_dict	)
   # create random numpy tensors
   _snake_case							:	Any			=						self.image_processor_tester.prepare_inputs(equal_resolution=a_,   numpify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   np.ndarray	)

   # Test not batched input
   _snake_case							:	str			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )

   # Test batched
   _snake_case							:	Optional[int]			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	int			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PyTorch tensors
   _snake_case							:	str			=						self.image_processor_tester.prepare_inputs(equal_resolution=a_,   torchify=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   torch.Tensor	)

   # Test not batched input
   _snake_case							:	Tuple			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )

   # Test batched
   _snake_case							:	str			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )







@require_torch
@require_vision
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ChineseCLIPImageProcessor if is_vision_available() else None


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						ChineseCLIPImageProcessingTester(self,   num_channels=4,   do_center_crop=a_	)
   _snake_case							:	Optional[int]			=						3


 @property
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.image_processing_class(**self.image_processor_dict	)
   self.assertTrue(hasattr(a_,   """do_resize"""	)	)
   self.assertTrue(hasattr(a_,   """size"""	)	)
   self.assertTrue(hasattr(a_,   """do_center_crop"""	)	)
   self.assertTrue(hasattr(a_,   """center_crop"""	)	)
   self.assertTrue(hasattr(a_,   """do_normalize"""	)	)
   self.assertTrue(hasattr(a_,   """image_mean"""	)	)
   self.assertTrue(hasattr(a_,   """image_std"""	)	)
   self.assertTrue(hasattr(a_,   """do_convert_rgb"""	)	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.image_processing_class(**self.image_processor_dict	)
   # create random PIL images
   _snake_case							:	List[str]			=						self.image_processor_tester.prepare_inputs(equal_resolution=a_	)
   for image in image_inputs:
     self.assertIsInstance(a_,   Image.Image	)

   # Test not batched input
   _snake_case							:	Dict			=						image_processing(image_inputs[0],   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           1,
           self.expected_encoded_image_num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )

   # Test batched
   _snake_case							:	List[str]			=						image_processing(a_,   return_tensors="""pt"""	).pixel_values
   self.assertEqual(
       encoded_images.shape,   (
           self.image_processor_tester.batch_size,
           self.expected_encoded_image_num_channels,
           self.image_processor_tester.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),   )








"""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       UpperCAmelCase__ ():
  """simple docstring"""

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

  return image


def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	str			=						[]
  # 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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      List[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						dct.pop(snake_case__					)
  _snake_case							:	Optional[int]			=						val


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      str					):
  """simple docstring"""

  for i in range(config.vision_config.num_hidden_layers					):
    # read in original q and v biases
    _snake_case							:	Optional[int]			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"					)
    _snake_case							:	Tuple			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"					)

    # next, set bias in the state dict
    _snake_case							:	List[str]			=						torch.cat((q_bias, torch.zeros_like(snake_case__	,     requires_grad=snake_case__					), v_bias)					)
    _snake_case							:	Dict			=						qkv_bias


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						3_64 if """coco""" in model_name else 2_24
  _snake_case							:	List[str]			=						BlipaVisionConfig(image_size=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:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-2.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "opt-6.7b" in model_name:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-6.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "t5-xl" in model_name:
    _snake_case							:	Tuple			=						TaConfig.from_pretrained("""google/flan-t5-xl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()
  elif "t5-xxl" in model_name:
    _snake_case							:	List[Any]			=						TaConfig.from_pretrained("""google/flan-t5-xxl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()

  _snake_case							:	int			=						BlipaConfig(vision_config=snake_case__	,     text_config=snake_case__					)

  return config, image_size


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int=None	,     snake_case__							:      str=False					):
  """simple docstring"""

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

  _snake_case							:	str			=						BlipaForConditionalGeneration(snake_case__					).eval()

  _snake_case							:	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"""),
  }

  _snake_case	,      _snake_case							:	List[Any]			=						model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""					)
  _snake_case							:	int			=						"""cuda""" if torch.cuda.is_available() else """cpu"""
  _snake_case	,      _snake_case	,      _snake_case							:	Any			=						load_model_and_preprocess(
      name=snake_case__	,     model_type=snake_case__	,     is_eval=snake_case__	,     device=snake_case__					)
  original_model.eval()
  print("""Done!"""					)

  # update state dict keys
  _snake_case							:	Any			=						original_model.state_dict()
  _snake_case							:	Dict			=						create_rename_keys(snake_case__					)
  for src, dest in rename_keys:
    rename_key(snake_case__	,     snake_case__	,     snake_case__					)

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

  # read in qv biases
  read_in_q_v_bias(snake_case__	,     snake_case__					)

  _snake_case	,      _snake_case							:	List[str]			=						hf_model.load_state_dict(snake_case__	,     strict=snake_case__					)
  assert len(snake_case__					) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _snake_case							:	Any			=						load_demo_image()
  _snake_case							:	str			=						vis_processors["""eval"""](snake_case__					).unsqueeze(0					).to(snake_case__					)
  _snake_case							:	List[Any]			=						tokenizer(["""\n"""]	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

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

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

  original_model.to(snake_case__					)
  hf_model.to(snake_case__					)
  with torch.no_grad():
    if "opt" in model_name:
      _snake_case							:	str			=						original_model({"""image""": original_pixel_values, """text_input""": [""""""]}					).logits
      _snake_case							:	int			=						hf_model(snake_case__	,     snake_case__					).logits
    else:
      _snake_case							:	str			=						original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}					).logits
      _snake_case							:	Optional[int]			=						input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,     -1_00					)
      _snake_case							:	Union[str, Any]			=						hf_model(snake_case__	,     snake_case__	,     labels=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":
    _snake_case							:	List[str]			=						torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]	,     device=snake_case__					)
    assert torch.allclose(logits[0, :3, :3]	,     snake_case__	,     atol=1e-4					)
  elif model_name == "blip2-flan-t5-xl-coco":
    _snake_case							:	Union[str, Any]			=						torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]	,     device=snake_case__					)
  else:
    # cast to same type
    _snake_case							:	int			=						logits.dtype
    assert torch.allclose(original_logits.to(snake_case__					)	,     snake_case__	,     atol=1e-2					)
  print("""Looks ok!"""					)

  print("""Generating a caption..."""					)
  _snake_case							:	Any			=						""""""
  _snake_case							:	str			=						tokenizer(snake_case__	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

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

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(snake_case__					)
    hf_model.save_pretrained(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__":
     A_      			=       argparse.ArgumentParser()
     A_      			=       [
         '''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''',
     )

     A_      			=       parser.parse_args()

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


