'''simple docstring'''





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

    if is_vision_available():
        from transformers import MaskFormerImageProcessor

if is_vision_available():
    from PIL import Image




class 			__A :



    def __init__(self       :       Tuple  ,     __a       :       Dict  ,     __a       :       Union[str, Any]=2  ,     __a       :       List[Any]=True  ,     __a       :       str=False  ,     __a       :       List[Any]=10  ,     __a       :       Optional[int]=3  ,     __a       :       Any=32 * 4  ,     __a       :       Any=32 * 6  ,     __a       :       Optional[int]=4  ,     __a       :       Optional[Any]=32  ,     ):
         UpperCAmelCase_			       =  parent
         UpperCAmelCase_			       =  batch_size
         UpperCAmelCase_			       =  is_training
         UpperCAmelCase_			       =  use_auxiliary_loss
         UpperCAmelCase_			       =  num_queries
         UpperCAmelCase_			       =  num_channels
         UpperCAmelCase_			       =  min_size
         UpperCAmelCase_			       =  max_size
         UpperCAmelCase_			       =  num_labels
         UpperCAmelCase_			       =  mask_feature_size



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
             __a					)

         UpperCAmelCase_			       =  torch.ones([self.batch_size, self.min_size, self.max_size]  ,     device=__a					)

         UpperCAmelCase_			       =  (
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,     device=__a					) > 0.5
         ).float()
         UpperCAmelCase_			       =  (torch.rand((self.batch_size, self.num_labels)  ,     device=__a					) > 0.5).long()

         UpperCAmelCase_			       =  self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels



    def    _lowercase		(self       :       Union[str, Any]					):
         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]  ,     )  ,     decoder_config=DetrConfig(
                 decoder_ffn_dim=128  ,     num_queries=self.num_queries  ,     decoder_attention_heads=2  ,     d_model=self.mask_feature_size  ,     )  ,     mask_feature_size=self.mask_feature_size  ,     fpn_feature_size=self.mask_feature_size  ,     num_channels=self.num_channels  ,     num_labels=self.num_labels  ,     )



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.prepare_config_and_inputs()
         UpperCAmelCase_			       =  {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
         return config, inputs_dict



    def    _lowercase		(self       :       int  ,     __a       :       int  ,     __a       :       Union[str, Any]					):
         UpperCAmelCase_			       =  output.encoder_hidden_states
         UpperCAmelCase_			       =  output.pixel_decoder_hidden_states
         UpperCAmelCase_			       =  output.transformer_decoder_hidden_states

         self.parent.assertTrue(len(__a					)  ,     len(config.backbone_config.depths					)					)
         self.parent.assertTrue(len(__a					)  ,     len(config.backbone_config.depths					)					)
         self.parent.assertTrue(len(__a					)  ,     config.decoder_config.decoder_layers					)



    def    _lowercase		(self       :       Tuple  ,     __a       :       Union[str, Any]  ,     __a       :       Any  ,     __a       :       Union[str, Any]  ,     __a       :       Any=False					):
         with torch.no_grad():
              UpperCAmelCase_			       =  MaskFormerModel(config=__a					)
              model.to(__a					)
              model.eval()

              UpperCAmelCase_			       =  model(pixel_values=__a  ,     pixel_mask=__a					)
              UpperCAmelCase_			       =  model(__a  ,     output_hidden_states=__a					)
         # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
         # encoder and pixel decoder
         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape  ,     (self.batch_size, self.num_queries, self.mask_feature_size)  ,     )
         # let's ensure the other two hidden state exists
         self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
         self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

         if output_hidden_states:
              self.check_output_hidden_state(__a  ,     __a					)






    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       int  ,     __a       :       List[str]  ,     __a       :       Dict  ,     __a       :       Any  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(config=__a					)
         model.to(__a					)
         model.eval()

         def comm_check_on_output(__a       :       Any					):
              # let's still check that all the required stuff is there
              self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
              self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
              self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
              # okay, now we need to check the logits shape
              # due to the encoder compression, masks have a //4 spatial size
              self.parent.assertEqual(
                  result.masks_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,     )
              # + 1 for null class
              self.parent.assertEqual(
                  result.class_queries_logits.shape  ,     (self.batch_size, self.num_queries, self.num_labels + 1)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(pixel_values=__a  ,     pixel_mask=__a					)
              UpperCAmelCase_			       =  model(__a					)

              comm_check_on_output(__a					)

              UpperCAmelCase_			       =  model(
                  pixel_values=__a  ,     pixel_mask=__a  ,     mask_labels=__a  ,     class_labels=__a					)

         comm_check_on_output(__a					)

         self.parent.assertTrue(result.loss is not None					)
         self.parent.assertEqual(result.loss.shape  ,     torch.Size([1]					)					)




@require_torch
class 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		List[str]			  =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    a__			:		List[Any]			  =    (
        {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

    a__			:		Optional[int]			  =    False
    a__			:		str			  =    False
    a__			:		Dict			  =    False
    a__			:		List[Any]			  =    False



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  MaskFormerModelTester(self					)
         UpperCAmelCase_			       =  ConfigTester(self  ,     config_class=__a  ,     has_text_modality=__a					)



    def    _lowercase		(self       :       Optional[Any]					):
         self.config_tester.run_common_tests()



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__a  ,     **__a  ,     output_hidden_states=__a					)



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__a					)



    @unittest.skip(reason="MaskFormer does not use inputs_embeds"					)
    def    _lowercase		(self       :       str					):
         pass



    @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"					)
    def    _lowercase		(self       :       Any					):
         pass



    @unittest.skip(reason="MaskFormer is not a generative model"					)
    def    _lowercase		(self       :       str					):
         pass



    @unittest.skip(reason="MaskFormer does not use token embeddings"					)
    def    _lowercase		(self       :       Any					):
         pass



    @require_torch_multi_gpu
    @unittest.skip(
        reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"					)
    def    _lowercase		(self       :       List[Any]					):
         pass



    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
    def    _lowercase		(self       :       str					):
         pass



    def    _lowercase		(self       :       int					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_			       =  model_class(__a					)
              UpperCAmelCase_			       =  inspect.signature(model.forward					)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_			       =  [*signature.parameters.keys()]

              UpperCAmelCase_			       =  ["pixel_values"]
              self.assertListEqual(arg_names[:1]  ,     __a					)



    @slow
    def    _lowercase		(self       :       List[str]					):
         for model_name in ["facebook/maskformer-swin-small-coco"]:
              UpperCAmelCase_			       =  MaskFormerModel.from_pretrained(__a					)
              self.assertIsNotNone(__a					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  (self.model_tester.min_size,) * 2
         UpperCAmelCase_			       =  {
             "pixel_values": torch.randn((2, 3, *size)  ,     device=__a					),
             "mask_labels": torch.randn((2, 10, *size)  ,     device=__a					),
             "class_labels": torch.zeros(2  ,     10  ,     device=__a					).long(),
         }

         UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(__a					)
         UpperCAmelCase_			       =  model(**__a					)
         self.assertTrue(outputs.loss is not None					)



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__a  ,     **__a  ,     output_hidden_states=__a					)



    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_			       =  model_class(__a					).to(__a					)
              UpperCAmelCase_			       =  model(**__a  ,     output_attentions=__a					)
              self.assertTrue(outputs.attentions is not None					)



    def    _lowercase		(self       :       List[Any]					):
         if not self.model_tester.is_training:
              return
         # only MaskFormerForInstanceSegmentation has the loss
         UpperCAmelCase_			       =  self.all_model_classes[1]
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()

         UpperCAmelCase_			       =  model_class(__a					)
         model.to(__a					)
         model.train()

         UpperCAmelCase_			       =  model(__a  ,     mask_labels=__a  ,     class_labels=__a					).loss
         loss.backward()






    def    _lowercase		(self       :       Optional[Any]					):
         # only MaskFormerForInstanceSegmentation has the loss
         UpperCAmelCase_			       =  self.all_model_classes[1]
         UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  self.model_tester.prepare_config_and_inputs()
         UpperCAmelCase_			       =  True
         UpperCAmelCase_			       =  True

         UpperCAmelCase_			       =  model_class(__a					)
         model.to(__a					)
         model.train()

         UpperCAmelCase_			       =  model(__a  ,     mask_labels=__a  ,     class_labels=__a					)

         UpperCAmelCase_			       =  outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         UpperCAmelCase_			       =  outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()
         # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
         UpperCAmelCase_			       =  outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         UpperCAmelCase_			       =  outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=__a					)

         self.assertIsNotNone(encoder_hidden_states.grad					)
         self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
         self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
         self.assertIsNotNone(attentions.grad					)




SCREAMING_SNAKE_CASE_:	int    =1E-4





def       lowerCAmelCase_							(   )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     return image






@require_vision
@slow
class 			__A (					unittest.TestCase      ):



    @cached_property
    def    _lowercase		(self       :       str					):
         return (
             MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"					)
             if is_vision_available()
             else None
         )



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"					).to(__a					)
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)

         UpperCAmelCase_			       =  torch.tensor(
             [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)

         UpperCAmelCase_			       =  torch.tensor(
             [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)

         UpperCAmelCase_			       =  torch.tensor(
             [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]					).to(__a					)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,     __a  ,     atol=__a					)					)



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)
         # masks_queries_logits
         UpperCAmelCase_			       =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
         UpperCAmelCase_			       =  [
             [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
             [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
             [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
         ]
         UpperCAmelCase_			       =  torch.tensor(__a					).to(__a					)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)
         # class_queries_logits
         UpperCAmelCase_			       =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
         UpperCAmelCase_			       =  torch.tensor(
             [
                 [1.6512E00, -5.2572E00, -3.3519E00],
                 [3.6169E-02, -5.9025E00, -2.9313E00],
                 [1.0766E-04, -7.7630E00, -5.1263E00],
             ]					).to(__a					)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     __a  ,     atol=__a					)					)



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor
         UpperCAmelCase_			       =  prepare_img()
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).to(__a					)
         UpperCAmelCase_			       =  inputs["pixel_values"].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
         # check size
         self.assertEqual(__a  ,     (1, 3, 800, 1088)					)

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)
         # masks_queries_logits
         UpperCAmelCase_			       =  outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,     )
         UpperCAmelCase_			       =  [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
         UpperCAmelCase_			       =  torch.tensor(__a					).to(__a					)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,     __a  ,     atol=__a					)					)
         # class_queries_logits
         UpperCAmelCase_			       =  outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape  ,     (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
         UpperCAmelCase_			       =  torch.tensor(
             [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]					).to(__a					)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,     __a  ,     atol=__a					)					)






    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  (
             MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"					)
             .to(__a					)
             .eval()
         )
         UpperCAmelCase_			       =  self.default_image_processor

         UpperCAmelCase_			       =  image_processor(
             [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]  ,     segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]  ,     return_tensors="pt"  ,     )

         UpperCAmelCase_			       =  inputs["pixel_values"].to(__a					)
         UpperCAmelCase_			       =  [el.to(__a					) for el in inputs["mask_labels"]]
         UpperCAmelCase_			       =  [el.to(__a					) for el in inputs["class_labels"]]

         with torch.no_grad():
              UpperCAmelCase_			       =  model(**__a					)

         self.assertTrue(outputs.loss is not None					)






"""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)