


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase      =   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''</s>'''
       snake_case_	=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase )		,				_UpperCAmelCase )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase )		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]		,				'''<pad>''' )
       self.assertEqual(vocab_keys[1]		,				'''</s>''' )
       self.assertEqual(vocab_keys[-1]		,				'''v''' )
       self.assertEqual(len(_UpperCAmelCase )		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case_	=				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       snake_case_	=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_61_03
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 1_03
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 10_24
       snake_case_	=				'''To ensure a smooth flow of bank resolutions.'''
       snake_case_	=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 1_50, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 10_24)
       assert batch.attention_mask.shape == (2, 10_24)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 @slow
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase		,				model_name='''google/bigbird-pegasus-large-arxiv'''		,				revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,				)







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase		,				offset=0		,				mask_token_sent=_UpperCAmelCase		,				mask_token='''[MASK]''' )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return PegasusTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 10_00, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 40_96)
       assert batch.attention_mask.shape == (2, 40_96)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				(
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       snake_case_	=				self._large_tokenizer(_UpperCAmelCase ).input_ids

       self.assertListEqual(
           _UpperCAmelCase		,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,				)



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 	__lowerCAmelCase   ()->  Optional[Any]:




      """simple docstring"""

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

      return image






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				[]
      # 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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				dct.pop(SCREAMING_SNAKE_CASE )
      snake_case_	=				val






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

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

      snake_case_	=				BlipaConfig(vision_config=SCREAMING_SNAKE_CASE , text_config=SCREAMING_SNAKE_CASE )

      return config, image_size






@torch.no_grad()
def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False )->  List[Any]:




      """simple docstring"""

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

      snake_case_	=				BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE ).eval()

      snake_case_	=				{
          '''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_	=				model_name_to_original[model_name]

      # load original model
      print('''Loading original model...''' )
      snake_case_	=				'''cuda''' if torch.cuda.is_available() else '''cpu'''
      snake_case_	,   snake_case_	,   snake_case_	=				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
      snake_case_	=				original_model.state_dict()
      snake_case_	=				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():
            snake_case_	=				state_dict.pop(SCREAMING_SNAKE_CASE )
            if key.startswith('''Qformer.bert''' ):
                  snake_case_	=				key.replace('''Qformer.bert''' , '''qformer''' )
            if "attention.self" in key:
                  snake_case_	=				key.replace('''self''' , '''attention''' )
            if "opt_proj" in key:
                  snake_case_	=				key.replace('''opt_proj''' , '''language_projection''' )
            if "t5_proj" in key:
                  snake_case_	=				key.replace('''t5_proj''' , '''language_projection''' )
            if key.startswith('''opt''' ):
                  snake_case_	=				key.replace('''opt''' , '''language''' )
            if key.startswith('''t5''' ):
                  snake_case_	=				key.replace('''t5''' , '''language''' )
            snake_case_	=				val

      # read in qv biases
      read_in_q_v_bias(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      snake_case_	,   snake_case_	=				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"]

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

      # create processor
      snake_case_	=				BlipImageProcessor(
          size={'''height''': image_size, '''width''': image_size} , image_mean=SCREAMING_SNAKE_CASE , image_std=SCREAMING_SNAKE_CASE )
      snake_case_	=				BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
      snake_case_	=				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:
                  snake_case_	=				original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
                  snake_case_	=				hf_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).logits
            else:
                  snake_case_	=				original_model(
                      {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
                  snake_case_	=				input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
                  snake_case_	=				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":
            snake_case_	=				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=SCREAMING_SNAKE_CASE )
            assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 )
      elif model_name == "blip2-flan-t5-xl-coco":
            snake_case_	=				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=SCREAMING_SNAKE_CASE )
      else:
            # cast to same type
            snake_case_	=				logits.dtype
            assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE , atol=1E-2 )
      print('''Looks ok!''' )

      print('''Generating a caption...''' )
      snake_case_	=				''''''
      snake_case_	=				tokenizer(SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_ids.to(SCREAMING_SNAKE_CASE )

      snake_case_	=				original_model.generate({'''image''': original_pixel_values} )
      snake_case_	=				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 )
      snake_case_	=				input_ids.shape[1]
      snake_case_	=				processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=SCREAMING_SNAKE_CASE )
      snake_case_	=				[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__":
   UpperCAmelCase      =   argparse.ArgumentParser()
   UpperCAmelCase      =   [
       """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      =   parser.parse_args()

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