





import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint




def _a (  SCREAMING_SNAKE_CASE__      :  Dict       )	->    List[str]:

       '''simple docstring'''






       return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()       )




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  str       )	->    Any:

       '''simple docstring'''






       SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{}

       for key, value in state_dict.items():
              if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                     continue

              SCREAMING_SNAKE_CASE__       :      List[Any]     =						key.replace("heads.cmd.mim_head.cls.predictions"			,				"mmm_image_head"       )
              SCREAMING_SNAKE_CASE__       :      List[str]     =						key.replace("heads.cmd.mlm_head.cls.predictions"			,				"mmm_text_head"       )
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						key.replace("heads.cmd.itm_head.cls"			,				"itm_head"       )
              SCREAMING_SNAKE_CASE__       :      Dict     =						key.replace("heads.cmd.itm_head.pooler"			,				"itm_head.pooler"       )
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						key.replace("heads.cmd.clip_head.logit_scale"			,				"flava.logit_scale"       )
              SCREAMING_SNAKE_CASE__       :      List[str]     =						key.replace("heads.fairseq_mlm.cls.predictions"			,				"mlm_head"       )
              SCREAMING_SNAKE_CASE__       :      str     =						key.replace("heads.imagenet.mim_head.cls.predictions"			,				"mim_head"       )
              SCREAMING_SNAKE_CASE__       :      Tuple     =						key.replace("mm_text_projection"			,				"flava.text_to_mm_projection"       )
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						key.replace("mm_image_projection"			,				"flava.image_to_mm_projection"       )
              SCREAMING_SNAKE_CASE__       :      List[str]     =						key.replace("image_encoder.module"			,				"flava.image_model"       )
              SCREAMING_SNAKE_CASE__       :      Any     =						key.replace("text_encoder.module"			,				"flava.text_model"       )
              SCREAMING_SNAKE_CASE__       :      List[str]     =						key.replace("mm_encoder.module.encoder.cls_token"			,				"flava.multimodal_model.cls_token"       )
              SCREAMING_SNAKE_CASE__       :      Dict     =						key.replace("mm_encoder.module"			,				"flava.multimodal_model"       )
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						key.replace("text_projection"			,				"flava.text_projection"       )
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						key.replace("image_projection"			,				"flava.image_projection"       )

              SCREAMING_SNAKE_CASE__       :      int     =						value.float()

       for key, value in codebook_state_dict.items():
              SCREAMING_SNAKE_CASE__       :      Dict     =						value

       return upgrade




@torch.no_grad()
def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple=None       )	->    Tuple:

       '''simple docstring'''






       if config_path is not None:
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						FlavaConfig.from_pretrained(a__       )
       else:
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						FlavaConfig()

       SCREAMING_SNAKE_CASE__       :      Tuple     =						FlavaForPreTraining(a__       ).eval()

       SCREAMING_SNAKE_CASE__       :      Tuple     =						convert_dalle_checkpoint(a__			,				a__			,				save_checkpoint=a__       )

       if os.path.exists(a__       ):
              SCREAMING_SNAKE_CASE__       :      Any     =						torch.load(a__			,				map_location="cpu"       )
       else:
              SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.hub.load_state_dict_from_url(a__			,				map_location="cpu"       )

       SCREAMING_SNAKE_CASE__       :      Optional[int]     =						upgrade_state_dict(a__			,				a__       )
       hf_model.load_state_dict(a__       )
       SCREAMING_SNAKE_CASE__       :      List[str]     =						hf_model.state_dict()
       SCREAMING_SNAKE_CASE__       :      Optional[int]     =						count_parameters(a__       )
       SCREAMING_SNAKE_CASE__       :      int     =						count_parameters(a__       ) + count_parameters(a__       )

       assert torch.allclose(a__			,				a__			,				atol=1E-3       )

       hf_model.save_pretrained(a__       )


if __name__ == "__main__":
  _lowerCamelCase :    int  							= argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
  parser.add_argument('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  _lowerCamelCase :    Tuple  							= parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)













import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[Any]=1_3,			_UpperCAmelCase     :					Optional[Any]=3_0,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=3,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[int]=True,			_UpperCAmelCase     :					Optional[Any]=3_2,			_UpperCAmelCase     :					Any=5,			_UpperCAmelCase     :					Optional[Any]=4,			_UpperCAmelCase     :					List[Any]=3_7,			_UpperCAmelCase     :					Optional[int]="gelu",			_UpperCAmelCase     :					int=0.1,			_UpperCAmelCase     :					List[str]=0.1,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					List[Any]=0.02,			_UpperCAmelCase     :					List[Any]=None,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						parent
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      str     =						image_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						patch_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_channels
														SCREAMING_SNAKE_CASE__       :      List[str]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Any     =						use_labels
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      str     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      str     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Any     =						type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Any     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Any     =						scope

														# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														SCREAMING_SNAKE_CASE__       :      str     =						(image_size // patch_size) ** 2
														SCREAMING_SNAKE_CASE__       :      str     =						num_patches + 1






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						None
														if self.use_labels:
																					SCREAMING_SNAKE_CASE__       :      int     =						ids_tensor([self.batch_size],			self.type_sequence_label_size						)

														SCREAMING_SNAKE_CASE__       :      int     =						self.get_config()

														return config, pixel_values, labels






							def  A_     ( self     :					int						)				->			Tuple:





														"""simple docstring"""
														return ViTMSNConfig(
														    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,			hidden_act=self.hidden_act,			hidden_dropout_prob=self.hidden_dropout_prob,			attention_probs_dropout_prob=self.attention_probs_dropout_prob,			initializer_range=self.initializer_range,			)






							def  A_     ( self     :					Dict,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNModel(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.type_sequence_label_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()
														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase,			labels=_UpperCAmelCase						)
														print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}"						)
														print("Labels: {labels}"						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)

														# test greyscale images
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ViTMSNForImageClassification(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)						)






							def  A_     ( self     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"pixel_values": pixel_values}
														return config, inputs_dict


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							UpperCAmelCase_      						=   (
							    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      str     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase,			hidden_size=3_7						)






							def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							@unittest.skip(reason="ViTMSN does not use inputs_embeds"						)
							def  A_     ( self     :					List[str]						)				->			Tuple:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model_class(_UpperCAmelCase						)
																					self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_UpperCAmelCase,			nn.Linear						)						)






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      str     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					Optional[int]						)				->			List[Any]:





														"""simple docstring"""
														for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ViTMSNModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)




def _a (  )	->    Dict:

							'''simple docstring'''






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







@require_torch
@require_vision
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"						) if is_vision_available() else None






							@slow
							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														torch.manual_seed(2						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"						).to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Dict     =						image_processor(images=_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Dict     =						model(**_UpperCAmelCase						)

														# verify the logits
														SCREAMING_SNAKE_CASE__       :      Tuple     =						torch.Size((1, 1_0_0_0)						)
														self.assertEqual(outputs.logits.shape,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor([-0.0803, -0.4454, -0.2375]						).to(_UpperCAmelCase						)

														self.assertTrue(torch.allclose(outputs.logits[0, :3],			_UpperCAmelCase,			atol=1E-4						)						)






