




import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class     lowerCamelCase__(					UpperCamelCase_ ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   MobileBertTokenizer
      UpperCAmelCase__      :	Union[str, Any]    		=   MobileBertTokenizerFast
      UpperCAmelCase__      :	List[str]    		=   True
      UpperCAmelCase__      :	Union[str, Any]    		=   True
      UpperCAmelCase__      :	Any    		=   filter_non_english
      UpperCAmelCase__      :	Optional[Any]    		=   'google/mobilebert-uncased'
      def 			lowerCAmelCase__						(				self: Tuple   ):
             super().setUp()

             __lowerCamelCase   =						[
                 '''[UNK]''',
                 '''[CLS]''',
                 '''[SEP]''',
                 '''[PAD]''',
                 '''[MASK]''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             __lowerCamelCase   =						os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]   )
             with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""   ) as vocab_writer:
                    vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]   )   )

             __lowerCamelCase   =						[
                 (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
                 for tokenizer_def in self.tokenizers_list
             ]
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: int   ):
             __lowerCamelCase   =						'''UNwant\u00E9d,running'''
             __lowerCamelCase   =						'''unwanted, running'''
             return input_text, output_text
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.tokenizer_class(self.vocab_file   )

             __lowerCamelCase   =						tokenizer.tokenize("""UNwant\u00E9d,running"""   )
             self.assertListEqual(UpperCamelCase__    ,       ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]   )
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__   )    ,       [9, 6, 7, 12, 10, 11]   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):





             if not self.test_rust_tokenizer:
                    return

             __lowerCamelCase   =						self.get_tokenizer()
             __lowerCamelCase   =						self.get_rust_tokenizer()

             __lowerCamelCase   =						'''UNwant\u00E9d,running'''

             __lowerCamelCase   =						tokenizer.tokenize(UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.tokenize(UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

             __lowerCamelCase   =						self.get_rust_tokenizer()
             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

             # With lower casing
             __lowerCamelCase   =						self.get_tokenizer(do_lower_case=UpperCamelCase__   )
             __lowerCamelCase   =						self.get_rust_tokenizer(do_lower_case=UpperCamelCase__   )

             __lowerCamelCase   =						'''UNwant\u00E9d,running'''

             __lowerCamelCase   =						tokenizer.tokenize(UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.tokenize(UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

             __lowerCamelCase   =						self.get_rust_tokenizer()
             __lowerCamelCase   =						tokenizer.encode(UpperCamelCase__   )
             __lowerCamelCase   =						rust_tokenizer.encode(UpperCamelCase__   )
             self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						BasicTokenizer()

             self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""   )    ,       ["""ah""", """\u535A""", """\u63A8""", """zz"""]   )
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """   )    ,       ["""hello""", """!""", """how""", """are""", """you""", """?"""]   )
             self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )    ,       ["""hello"""]   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__    ,       strip_accents=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )    ,       ["""hällo""", """!""", """how""", """are""", """you""", """?"""]   )
             self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )    ,       ["""h\u00E9llo"""]   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__    ,       strip_accents=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )    ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""]   )
             self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )    ,       ["""hello"""]   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )    ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""]   )
             self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""   )    ,       ["""hello"""]   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """   )    ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__    ,       strip_accents=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )    ,       ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__    ,       strip_accents=UpperCamelCase__   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """   )    ,       ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						BasicTokenizer(do_lower_case=UpperCamelCase__    ,       never_split=["""[UNK]"""]   )

             self.assertListEqual(
                 tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""   )    ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

             __lowerCamelCase   =						{}
             for i, token in enumerate(UpperCamelCase__   ):
                    __lowerCamelCase   =						i
             __lowerCamelCase   =						WordpieceTokenizer(vocab=UpperCamelCase__    ,       unk_token="""[UNK]"""   )

             self.assertListEqual(tokenizer.tokenize(""""""   )    ,       []   )

             self.assertListEqual(tokenizer.tokenize("""unwanted running"""   )    ,       ["""un""", """##want""", """##ed""", """runn""", """##ing"""]   )

             self.assertListEqual(tokenizer.tokenize("""unwantedX running"""   )    ,       ["""[UNK]""", """runn""", """##ing"""]   )
      def 			lowerCAmelCase__						(				self: str   ):
             self.assertTrue(_is_whitespace(""" """   )   )
             self.assertTrue(_is_whitespace("""\t"""   )   )
             self.assertTrue(_is_whitespace("""\r"""   )   )
             self.assertTrue(_is_whitespace("""\n"""   )   )
             self.assertTrue(_is_whitespace("""\u00A0"""   )   )

             self.assertFalse(_is_whitespace("""A"""   )   )
             self.assertFalse(_is_whitespace("""-"""   )   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             self.assertTrue(_is_control("""\u0005"""   )   )

             self.assertFalse(_is_control("""A"""   )   )
             self.assertFalse(_is_control(""" """   )   )
             self.assertFalse(_is_control("""\t"""   )   )
             self.assertFalse(_is_control("""\r"""   )   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             self.assertTrue(_is_punctuation("""-"""   )   )
             self.assertTrue(_is_punctuation("""$"""   )   )
             self.assertTrue(_is_punctuation("""`"""   )   )
             self.assertTrue(_is_punctuation("""."""   )   )

             self.assertFalse(_is_punctuation("""A"""   )   )
             self.assertFalse(_is_punctuation(""" """   )   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						self.get_tokenizer()
             __lowerCamelCase   =						self.get_rust_tokenizer()

             # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
             self.assertListEqual([tokenizer.tokenize(UpperCamelCase__   ) for t in ["""Test""", """\xad""", """test"""]]    ,       [["""[UNK]"""], [], ["""[UNK]"""]]   )

             self.assertListEqual(
                 [rust_tokenizer.tokenize(UpperCamelCase__   ) for t in ["""Test""", """\xad""", """test"""]]    ,       [["""[UNK]"""], [], ["""[UNK]"""]]   )
      @slow
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						self.tokenizer_class.from_pretrained("""google/mobilebert-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase__   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase__   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase__   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase__    ,       UpperCamelCase__   )

             assert encoded_sentence == [1_01] + text + [1_02]
             assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
      def 			lowerCAmelCase__						(				self: Any   ):





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                           __lowerCamelCase   =						self.rust_tokenizer_class.from_pretrained(UpperCamelCase__    ,       **UpperCamelCase__   )

                           __lowerCamelCase   =						F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
                           __lowerCamelCase   =						tokenizer_r.encode_plus(
                               UpperCamelCase__    ,       return_attention_mask=UpperCamelCase__    ,       return_token_type_ids=UpperCamelCase__    ,       return_offsets_mapping=UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__    ,       )

                           __lowerCamelCase   =						tokenizer_r.do_lower_case if hasattr(UpperCamelCase__    ,       """do_lower_case"""   ) else False
                           __lowerCamelCase   =						(
                               [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), '''A'''),
                                   ((1, 2), ''','''),
                                   ((3, 5), '''na'''),
                                   ((5, 6), '''##ï'''),
                                   ((6, 8), '''##ve'''),
                                   ((9, 15), tokenizer_r.mask_token),
                                   ((16, 21), '''Allen'''),
                                   ((21, 23), '''##NL'''),
                                   ((23, 24), '''##P'''),
                                   ((25, 33), '''sentence'''),
                                   ((33, 34), '''.'''),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                               if not do_lower_case
                               else [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), '''a'''),
                                   ((1, 2), ''','''),
                                   ((3, 8), '''naive'''),
                                   ((9, 15), tokenizer_r.mask_token),
                                   ((16, 21), '''allen'''),
                                   ((21, 23), '''##nl'''),
                                   ((23, 24), '''##p'''),
                                   ((25, 33), '''sentence'''),
                                   ((33, 34), '''.'''),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                           )

                           self.assertEqual(
                               [e[1] for e in expected_results]    ,       tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]   )   )
                           self.assertEqual([e[0] for e in expected_results]    ,       tokens["""offset_mapping"""]   )




      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						['''的''', '''人''', '''有''']
             __lowerCamelCase   =						''''''.join(UpperCamelCase__   )
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                           __lowerCamelCase   =						True
                           __lowerCamelCase   =						self.tokenizer_class.from_pretrained(UpperCamelCase__    ,       **UpperCamelCase__   )
                           __lowerCamelCase   =						self.rust_tokenizer_class.from_pretrained(UpperCamelCase__    ,       **UpperCamelCase__   )

                           __lowerCamelCase   =						tokenizer_p.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
                           __lowerCamelCase   =						tokenizer_r.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )

                           __lowerCamelCase   =						tokenizer_r.convert_ids_to_tokens(UpperCamelCase__   )
                           __lowerCamelCase   =						tokenizer_p.convert_ids_to_tokens(UpperCamelCase__   )

                           # it is expected that each Chinese character is not preceded by "##"
                           self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )
                           self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )

                           __lowerCamelCase   =						False
                           __lowerCamelCase   =						self.rust_tokenizer_class.from_pretrained(UpperCamelCase__    ,       **UpperCamelCase__   )
                           __lowerCamelCase   =						self.tokenizer_class.from_pretrained(UpperCamelCase__    ,       **UpperCamelCase__   )

                           __lowerCamelCase   =						tokenizer_r.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )
                           __lowerCamelCase   =						tokenizer_p.encode(UpperCamelCase__    ,       add_special_tokens=UpperCamelCase__   )

                           __lowerCamelCase   =						tokenizer_r.convert_ids_to_tokens(UpperCamelCase__   )
                           __lowerCamelCase   =						tokenizer_p.convert_ids_to_tokens(UpperCamelCase__   )

                           # it is expected that only the first Chinese character is not preceded by "##".
                           __lowerCamelCase   =						[
                               F'##{token}' if idx != 0 else token for idx, token in enumerate(UpperCamelCase__   )
                           ]
                           self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )
                           self.assertListEqual(UpperCamelCase__    ,       UpperCamelCase__   )


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						).convert('''RGB'''						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)		,  interpolation=InterpolationMode.BICUBIC						),
						        transforms.ToTensor(),
						        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)		,  (0.2686_2954, 0.2613_0258, 0.2757_7711)						),
						    ]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							transform(_lowercase						).unsqueeze(0						).to(_lowercase						)
						return image
def       A	(    _lowercase						):
						if "visual_encoder" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub('''visual_encoder*'''		,  '''vision_model.encoder'''		,  _lowercase						)
						if "blocks" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''blocks'''		,  '''layers'''		,  _lowercase						)
						if "attn" in key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							re.sub(R'''attn'''		,  '''self_attn'''		,  _lowercase						)
						if "norm1" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''norm1'''		,  '''layer_norm1'''		,  _lowercase						)
						if "norm2" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''norm2'''		,  '''layer_norm2'''		,  _lowercase						)
						if "encoder.norm" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub(R'''encoder.norm'''		,  '''post_layernorm'''		,  _lowercase						)
						if "encoder.patch_embed.proj" in key:
												SCREAMING_SNAKE_CASE							:    Dict							=							re.sub(R'''encoder.patch_embed.proj'''		,  '''embeddings.patch_embedding'''		,  _lowercase						)

						if "encoder.pos_embed" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''encoder.pos_embed'''		,  '''embeddings.position_embedding'''		,  _lowercase						)
						if "encoder.cls_token" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''encoder.cls_token'''		,  '''embeddings.class_embedding'''		,  _lowercase						)

						if "self_attn" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''self_attn.proj'''		,  '''self_attn.projection'''		,  _lowercase						)

						return key
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase=None						):
						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Any							=							BlipConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlipConfig(projection_dim=512		,  text_config={}		,  vision_config={}						)

						SCREAMING_SNAKE_CASE							:    str							=							BlipForConditionalGeneration(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_decoder(pretrained=_lowercase		,  image_size=384		,  vit='''base'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							pt_model.eval()

						SCREAMING_SNAKE_CASE							:    Dict							=							pt_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Tuple							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Any							=							value

						hf_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							384
						SCREAMING_SNAKE_CASE							:    Tuple							=							load_demo_image(image_size=_lowercase		,  device='''cpu'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							BertTokenizer.from_pretrained('''bert-base-uncased'''						)
						SCREAMING_SNAKE_CASE							:    int							=							tokenizer(['''a picture of''']						).input_ids

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							hf_model.generate(_lowercase		,  _lowercase						)

						assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_model.generate(_lowercase						)

						assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(_lowercase						)

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						SCREAMING_SNAKE_CASE							:    str							=							(
						    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_vqa(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						vqa_model.eval()

						SCREAMING_SNAKE_CASE							:    List[str]							=							vqa_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							value

						SCREAMING_SNAKE_CASE							:    Dict							=							BlipForQuestionAnswering(_lowercase						)

						hf_vqa_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Dict							=							['''How many dogs are in this image?''']
						SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(_lowercase		,  return_tensors='''pt'''						).input_ids

						SCREAMING_SNAKE_CASE							:    Dict							=							hf_vqa_model.generate(_lowercase		,  _lowercase						)
						print(tokenizer.decode(answer[0]						)						)

						assert tokenizer.decode(answer[0]						) == "[UNK] 1 [SEP]"
						if pytorch_dump_folder_path is not None:
												hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''						)

						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

						SCREAMING_SNAKE_CASE							:    int							=							blip_itm(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						itm_model.eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							itm_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    int							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Dict							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value

						SCREAMING_SNAKE_CASE							:    int							=							BlipForImageTextRetrieval(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							['''A picture of a woman with a dog sitting in a beach''']
						SCREAMING_SNAKE_CASE							:    str							=							tokenizer(
						    _lowercase		,  return_tensors='''pt'''		,  padding='''max_length'''		,  truncation=_lowercase		,  max_length=35		,  ).input_ids

						hf_itm_model.load_state_dict(_lowercase						)
						hf_itm_model.eval()

						SCREAMING_SNAKE_CASE							:    int							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)

						assert out[0].item() == 0.2110_6874_9427_7954
						assert torch.nn.functional.softmax(out_itm[0]		,  dim=1						)[:, 1].item() == 0.4_5698_8453_8650_5127

						if pytorch_dump_folder_path is not None:
												hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''						)


if __name__ == "__main__":
					__UpperCamelCase		:       Optional[int]									=     argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					__UpperCamelCase		:       int									=     parser.parse_args()

					convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





