




"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertConfig,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertWithLMHeadModel,
  )

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					)				->   List[Any]:
       UpperCamelCase              =	parent
       UpperCamelCase              =	1_3
       UpperCamelCase              =	7
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	False
       UpperCamelCase              =	False
       UpperCamelCase              =	False
       UpperCamelCase              =	2
       UpperCamelCase              =	9_9
       UpperCamelCase              =	0
       UpperCamelCase              =	3_2
       UpperCamelCase              =	2
       UpperCamelCase              =	4
       UpperCamelCase              =	0.1
       UpperCamelCase              =	0.1
       UpperCamelCase              =	5_1_2
       UpperCamelCase              =	1_6
       UpperCamelCase              =	2
       UpperCamelCase              =	0.02
       UpperCamelCase              =	3
       UpperCamelCase              =	4
       UpperCamelCase              =	'''last'''
       UpperCamelCase              =	True
       UpperCamelCase              =	None
       UpperCamelCase              =	0







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size)
       UpperCamelCase              =	random_attention_mask([self.batch_size, self.seq_length]       ,					dtype=tf.floataa)

       UpperCamelCase              =	None
       if self.use_input_lengths:
           UpperCamelCase              =	(
               ids_tensor([self.batch_size]       ,					vocab_size=2) + self.seq_length - 2
           )  # small variation of seq_length

       UpperCamelCase              =	None
       if self.use_token_type_ids:
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.n_langs)

       UpperCamelCase              =	None
       UpperCamelCase              =	None
       UpperCamelCase              =	None
       if self.use_labels:
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.type_sequence_label_size)
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					2       ,					dtype=tf.floataa)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.num_choices)

       UpperCamelCase              =	FlaubertConfig(
           vocab_size=self.vocab_size       ,					n_special=self.n_special       ,					emb_dim=self.hidden_size       ,					n_layers=self.num_hidden_layers       ,					n_heads=self.num_attention_heads       ,					dropout=self.hidden_dropout_prob       ,					attention_dropout=self.attention_probs_dropout_prob       ,					gelu_activation=self.gelu_activation       ,					sinusoidal_embeddings=self.sinusoidal_embeddings       ,					asm=self.asm       ,					causal=self.causal       ,					n_langs=self.n_langs       ,					max_position_embeddings=self.max_position_embeddings       ,					initializer_range=self.initializer_range       ,					summary_type=self.summary_type       ,					use_proj=self.use_proj       ,					bos_token_id=self.bos_token_id       ,					)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	TFFlaubertModel(config=lowerCamelCase_)
       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)

       UpperCamelCase              =	[input_ids, input_mask]
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Tuple:
       UpperCamelCase              =	TFFlaubertWithLMHeadModel(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)

       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	TFFlaubertForQuestionAnsweringSimple(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths}

       UpperCamelCase              =	model(lowerCamelCase_)

       self.parent.assertEqual(result.start_logits.shape       ,					(self.batch_size, self.seq_length))
       self.parent.assertEqual(result.end_logits.shape       ,					(self.batch_size, self.seq_length))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   str:
       UpperCamelCase              =	TFFlaubertForSequenceClassification(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths}

       UpperCamelCase              =	model(lowerCamelCase_)

       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	TFFlaubertForTokenClassification(config=lowerCamelCase_)
       UpperCamelCase              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	self.num_choices
       UpperCamelCase              =	TFFlaubertForMultipleChoice(config=lowerCamelCase_)
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	{
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
           '''token_type_ids''': multiple_choice_token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices))







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	
       )              =	config_and_inputs
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''langs''': token_type_ids,
           '''lengths''': input_lengths,
       }
       return config, inputs_dict

@require_tf
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (
       (
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
           TFFlaubertForSequenceClassification,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForTokenClassification,
           TFFlaubertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   A_				      =       (
       (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A_				      =       (
       {
           '''feature-extraction''': TFFlaubertModel,
           '''fill-mask''': TFFlaubertWithLMHeadModel,
           '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
           '''text-classification''': TFFlaubertForSequenceClassification,
           '''token-classification''': TFFlaubertForTokenClassification,
           '''zero-shot''': TFFlaubertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_				      =       False
   A_				      =       False







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('''Fast''')
       ):
           # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
           # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
           # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
           return True

       return False







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	TFFlaubertModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					emb_dim=3_7)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       self.config_tester.run_common_tests()







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_token_classification(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowerCamelCase_)







   @slow
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	TFFlaubertModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)

@require_tf
@require_sentencepiece
@require_tokenizers
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   @slow
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

       UpperCamelCase              =	tf.convert_to_tensor(
           [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]]       ,					dtype=tf.intaa       ,					)  # "J'aime flaubert !"

       UpperCamelCase              =	model(lowerCamelCase_)[0]
       UpperCamelCase              =	tf.TensorShape((1, 8, 5_1_2))
       self.assertEqual(output.shape       ,					lowerCamelCase_)
       # compare the actual values for a slice.
       UpperCamelCase              =	tf.convert_to_tensor(
           [
               [
                   [-1.876_8773, -1.56_6555, 0.2707_2418],
                   [-1.692_0038, -0.587_3505, 1.932_9599],
                   [-2.956_3985, -1.699_3835, 1.797_2052],
               ]
           ]       ,					dtype=tf.floataa       ,					)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,					expected_slice.numpy()       ,					atol=1e-4))

"""simple docstring"""




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 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     ).convert('''RGB'''     )

		SCREAMING_SNAKE_CASE_    			=			transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size),				interpolation=InterpolationMode.BICUBIC     ),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3),				(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)     ),
		    ]     )
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     ).to(__a     )
		return image



def 							_lowerCamelCase			(			__a     ):
		if "visual_encoder" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub('''visual_encoder*''',				'''vision_model.encoder''',				__a     )
		if "blocks" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''blocks''',				'''layers''',				__a     )
		if "attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''attn''',				'''self_attn''',				__a     )
		if "norm1" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm1''',				'''layer_norm1''',				__a     )
		if "norm2" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm2''',				'''layer_norm2''',				__a     )
		if "encoder.norm" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.norm''',				'''post_layernorm''',				__a     )
		if "encoder.patch_embed.proj" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.patch_embed.proj''',				'''embeddings.patch_embedding''',				__a     )

		if "encoder.pos_embed" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.pos_embed''',				'''embeddings.position_embedding''',				__a     )
		if "encoder.cls_token" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.cls_token''',				'''embeddings.class_embedding''',				__a     )

		if "self_attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''self_attn.proj''',				'''self_attn.projection''',				__a     )

		return key



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a=None     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig(projection_dim=512,				text_config={},				vision_config={}     )

		SCREAMING_SNAKE_CASE_    			=			BlipForConditionalGeneration(__a     ).eval()

		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_decoder(pretrained=__a,				image_size=384,				vit='''base'''     )
		SCREAMING_SNAKE_CASE_    			=			pt_model.eval()

		SCREAMING_SNAKE_CASE_    			=			pt_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		hf_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			384
		SCREAMING_SNAKE_CASE_    			=			load_demo_image(image_size=__a,				device='''cpu'''     )
		SCREAMING_SNAKE_CASE_    			=			BertTokenizer.from_pretrained('''bert-base-uncased'''     )
		SCREAMING_SNAKE_CASE_    			=			tokenizer(['''a picture of''']     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_model.generate(__a,				__a     )

		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_    			=			hf_model.generate(__a     )

		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(__a     )

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

		SCREAMING_SNAKE_CASE_    			=			blip_vqa(pretrained=__a,				image_size=__a,				vit='''base'''     )
		vqa_model.eval()

		SCREAMING_SNAKE_CASE_    			=			vqa_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForQuestionAnswering(__a     )

		hf_vqa_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''How many dogs are in this image?''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(__a,				return_tensors='''pt'''     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_vqa_model.generate(__a,				__a     )
		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_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_itm(pretrained=__a,				image_size=__a,				vit='''base'''     )
		itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			itm_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForImageTextRetrieval(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''A picture of a woman with a dog sitting in a beach''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(
		    __a,				return_tensors='''pt''',				padding='''max_length''',				truncation=__a,				max_length=35,				).input_ids

		hf_itm_model.load_state_dict(__a     )
		hf_itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )
		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0],				dim=1     )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
						lowerCAmelCase__				=					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')
						lowerCAmelCase__				=					parser.parse_args()

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