






'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Dict=13			,							UpperCamelCase__:				int=30			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				Tuple=3			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=True			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				Tuple=4			,							UpperCamelCase__:				Optional[Any]=37			,							UpperCamelCase__:				List[Any]="gelu"			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				Tuple=10			,							UpperCamelCase__:				Optional[Any]=0.02			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				str=0.6			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	List[Any]         =       parent
														lowerCamelCase__					:	Optional[Any]         =       batch_size
														lowerCamelCase__					:	Union[str, Any]         =       image_size
														lowerCamelCase__					:	Any         =       patch_size
														lowerCamelCase__					:	Union[str, Any]         =       num_channels
														lowerCamelCase__					:	Optional[Any]         =       is_training
														lowerCamelCase__					:	int         =       use_labels
														lowerCamelCase__					:	List[str]         =       hidden_size
														lowerCamelCase__					:	Optional[Any]         =       num_hidden_layers
														lowerCamelCase__					:	Any         =       num_attention_heads
														lowerCamelCase__					:	Optional[Any]         =       intermediate_size
														lowerCamelCase__					:	Optional[Any]         =       hidden_act
														lowerCamelCase__					:	Any         =       hidden_dropout_prob
														lowerCamelCase__					:	Tuple         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Dict         =       type_sequence_label_size
														lowerCamelCase__					:	Optional[int]         =       initializer_range
														lowerCamelCase__					:	List[str]         =       mask_ratio
														lowerCamelCase__					:	Optional[int]         =       scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														lowerCamelCase__					:	Any         =       (image_size // patch_size) ** 2
														lowerCamelCase__					:	str         =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	str         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	List[Any]         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Union[str, Any]         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

														lowerCamelCase__					:	Optional[Any]         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Any     ):
														return ViTMAEConfig(
														    image_size=self.image_size			,							patch_size=self.patch_size			,							num_channels=self.num_channels			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							decoder_hidden_size=self.hidden_size			,							decoder_num_hidden_layers=self.num_hidden_layers			,							decoder_num_attention_heads=self.num_attention_heads			,							decoder_intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	Tuple         =       TFViTMAEModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       TFViTMAEForPreTraining(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														# expected sequence length = num_patches
														lowerCamelCase__					:	List[str]         =       (self.image_size // self.patch_size) ** 2
														lowerCamelCase__					:	Union[str, Any]         =       self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels)     )

														# test greyscale images
														lowerCamelCase__					:	List[Any]         =       1
														lowerCamelCase__					:	Union[str, Any]         =       TFViTMAEForPreTraining(UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       self.patch_size**2
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, num_patches, expected_num_channels)     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.prepare_config_and_inputs()
														((lowerCamelCase__)  ,		(lowerCamelCase__)  ,		(lowerCamelCase__))					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	List[Any]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							a				      =       {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	int         =       TFViTMAEModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(tf.keras.layers.Layer)     )
																					lowerCamelCase__					:	Dict         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							tf.keras.layers.Layer     )     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       inspect.signature(model.call     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	str         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Tuple         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														# make the mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Union[str, Any]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Optional[Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       copy.deepcopy(self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       outputs_dict[0].numpy()
																					lowerCamelCase__					:	Optional[int]         =       outputs_keywords[0].numpy()

																					self.assertLess(np.sum(np.abs(output_dict - output_keywords     )     )			,							1e-6     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														# make the mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Tuple         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Optional[Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														def prepare_numpy_arrays(UpperCamelCase__:				int     ):
																					lowerCamelCase__					:	Optional[int]         =       {}
																					for k, v in inputs_dict.items():
																												if tf.is_tensor(UpperCamelCase__     ):
																																			lowerCamelCase__					:	List[str]         =       v.numpy()
																												else:
																																			lowerCamelCase__					:	Union[str, Any]         =       np.array(UpperCamelCase__     )

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )

																					lowerCamelCase__					:	List[Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	str         =       prepare_numpy_arrays(UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )
																					self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				str     ):
														# make masks reproducible
														np.random.seed(2     )

														lowerCamelCase__					:	List[str]         =       int((tf_model.config.image_size // tf_model.config.patch_size) ** 2     )
														lowerCamelCase__					:	int         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
														lowerCamelCase__					:	Optional[int]         =       tf.constant(UpperCamelCase__     )

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														lowerCamelCase__					:	Tuple         =       tf_noise

														super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	List[Any]         =       {
														    module_member
														    for model_class in self.all_model_classes
														    for module in (import_module(model_class.__module__     ),)
														    for module_member_name in dir(UpperCamelCase__     )
														    if module_member_name.endswith("""MainLayer"""     )
														    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
														    and module_member_name[: -len("""MainLayer"""     )] == model_class.__name__[: -len("""Model"""     )]
														    for module_member in (getattr(UpperCamelCase__			,							UpperCamelCase__     ),)
														    if isinstance(UpperCamelCase__			,							UpperCamelCase__     )
														    and tf.keras.layers.Layer in module_member.__bases__
														    and getattr(UpperCamelCase__			,							"""_keras_serializable"""			,							UpperCamelCase__     )
														}

														lowerCamelCase__					:	List[str]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Dict         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
														lowerCamelCase__					:	List[str]         =       tf.convert_to_tensor(UpperCamelCase__     )
														inputs_dict.update({"""noise""": noise}     )

														for main_layer_class in tf_main_layer_classes:
																					lowerCamelCase__					:	List[str]         =       main_layer_class(UpperCamelCase__     )

																					lowerCamelCase__					:	int         =       {
																					    name: tf.keras.Input(tensor.shape[1:]			,							dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
																					}

																					lowerCamelCase__					:	List[str]         =       tf.keras.Model(UpperCamelCase__			,							outputs=main_layer(UpperCamelCase__     )     )
																					lowerCamelCase__					:	Union[str, Any]         =       model(UpperCamelCase__     )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												lowerCamelCase__					:	int         =       os.path.join(UpperCamelCase__			,							"""keras_model.h5"""     )
																												model.save(UpperCamelCase__     )
																												lowerCamelCase__					:	int         =       tf.keras.models.load_model(
																												    UpperCamelCase__			,							custom_objects={main_layer_class.__name__: main_layer_class}     )
																												assert isinstance(UpperCamelCase__			,							tf.keras.Model     )
																												lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__     )
																												self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Tuple         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	str         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Tuple         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					if model_class.__name__ == "TFViTMAEModel":
																												lowerCamelCase__					:	Any         =       outputs.last_hidden_state.numpy()
																												lowerCamelCase__					:	List[str]         =       0
																					else:
																												lowerCamelCase__					:	int         =       outputs.logits.numpy()
																												lowerCamelCase__					:	Dict         =       0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(UpperCamelCase__			,							saved_model=UpperCamelCase__     )
																												lowerCamelCase__					:	Dict         =       model_class.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																												if model_class.__name__ == "TFViTMAEModel":
																																			lowerCamelCase__					:	str         =       after_outputs["""last_hidden_state"""].numpy()
																																			lowerCamelCase__					:	Optional[Any]         =       0
																												else:
																																			lowerCamelCase__					:	Union[str, Any]         =       after_outputs["""logits"""].numpy()
																																			lowerCamelCase__					:	List[Any]         =       0

																												lowerCamelCase__					:	int         =       np.amax(np.abs(out_a - out_a     )     )
																												self.assertLessEqual(UpperCamelCase__			,							1e-5     )




							def 		lowerCamelCase_				(							self:				Any     ):
														# make mask reproducible
														np.random.seed(2     )

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__					:	Optional[int]         =       int((config.image_size // config.patch_size) ** 2     )
														lowerCamelCase__					:	Union[str, Any]         =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[int]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )

																					lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__			,							noise=UpperCamelCase__     )
																					lowerCamelCase__					:	List[Any]         =       model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(UpperCamelCase__     )
																					lowerCamelCase__					:	int         =       model_class.from_config(model.get_config()     )
																					# make sure it also accepts a normal config
																					lowerCamelCase__					:	int         =       model_class.from_config(model.config     )
																					lowerCamelCase__					:	List[Any]         =       new_model(UpperCamelCase__     )  # Build model
																					new_model.set_weights(model.get_weights()     )
																					lowerCamelCase__					:	List[Any]         =       new_model(UpperCamelCase__			,							noise=UpperCamelCase__     )

																					self.assert_outputs_same(UpperCamelCase__			,							UpperCamelCase__     )




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""     )
							def 		lowerCamelCase_				(							self:				List[str]     ):
														pass




							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Tuple         =       TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""     )
														self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Any         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_tf
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""     ) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														# make random mask reproducible across the PT and TF model
														np.random.seed(2     )

														lowerCamelCase__					:	Optional[int]         =       TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	int         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														lowerCamelCase__					:	Tuple         =       ViTMAEConfig()
														lowerCamelCase__					:	Dict         =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
														lowerCamelCase__					:	str         =       np.random.uniform(size=(1, num_patches)     )

														# forward pass
														lowerCamelCase__					:	str         =       model(**UpperCamelCase__			,							noise=UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Any         =       tf.convert_to_tensor([1, 196, 768]     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	str         =       tf.convert_to_tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]     )

														tf.debugging.assert_near(outputs.logits[0, :3, :3]			,							UpperCamelCase__			,							atol=1e-4     )


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)





