


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_A		       =						logging.get_logger(__name__)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					Any=False			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]=False				):
     __UpperCamelCase    							='backbone.' if is_semantic else ''

     __UpperCamelCase    							=[]
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight')				)
          rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias')				)
          rename_keys.append(
              (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight')				)
          rename_keys.append(
              (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias')				)
          rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight')				)
          rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias')				)
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight')				)
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias')				)
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight')				)
          rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (F'{prefix}cls_token', 'beit.embeddings.cls_token'),
             (F'{prefix}patch_embed.proj.weight', 'beit.embeddings.patch_embeddings.projection.weight'),
             (F'{prefix}patch_embed.proj.bias', 'beit.embeddings.patch_embeddings.projection.bias'),
             (F'{prefix}pos_embed', 'beit.embeddings.position_embeddings'),
         ]				)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ('mask_token', 'beit.embeddings.mask_token'),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]				)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
                  ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]				)

     return rename_keys

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]=False			,     SCREAMING_SNAKE_CASE__			:					int=False				):
     for i in range(config.num_hidden_layers				):
          __UpperCamelCase    							='backbone.' if is_semantic else ''
          # queries, keys and values
          __UpperCamelCase    							=state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'				)
          __UpperCamelCase    							=state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'				)
          __UpperCamelCase    							=state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'				)

          __UpperCamelCase    							=in_proj_weight[
              : config.hidden_size, :
          ]
          __UpperCamelCase    							=q_bias
          __UpperCamelCase    							=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __UpperCamelCase    							=in_proj_weight[
              -config.hidden_size :, :
          ]
          __UpperCamelCase    							=v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          __UpperCamelCase    							=state_dict.pop(F'{prefix}blocks.{i}.gamma_1'				)
          __UpperCamelCase    							=state_dict.pop(F'{prefix}blocks.{i}.gamma_2'				)

          __UpperCamelCase    							=gamma_a
          __UpperCamelCase    							=gamma_a

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=dct.pop(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=val

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							='http://images.cocodataset.org/val2017/000000039769.jpg'
     __UpperCamelCase    							=Image.open(requests.get(SCREAMING_SNAKE_CASE__			,     stream=SCREAMING_SNAKE_CASE__				).raw				)
     return im

@torch.no_grad()
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]=False				):
     __UpperCamelCase    							=False if 'rvlcdip' in checkpoint_url else True
     __UpperCamelCase    							=BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE__			,     use_mask_token=SCREAMING_SNAKE_CASE__				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          __UpperCamelCase    							=10_24
          __UpperCamelCase    							=40_96
          __UpperCamelCase    							=24
          __UpperCamelCase    							=16

     # labels
     if "rvlcdip" in checkpoint_url:
          __UpperCamelCase    							=16
          __UpperCamelCase    							='huggingface/label-files'
          __UpperCamelCase    							='rvlcdip-id2label.json'
          __UpperCamelCase    							=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     repo_type='dataset'				)			,     'r'				)				)
          __UpperCamelCase    							={int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
          __UpperCamelCase    							=idalabel
          __UpperCamelCase    							={v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     __UpperCamelCase    							=torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__			,     map_location='cpu'				)['model']

     __UpperCamelCase    							=create_rename_keys(SCREAMING_SNAKE_CASE__			,     has_lm_head=SCREAMING_SNAKE_CASE__				)
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     read_in_q_k_v(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     has_lm_head=SCREAMING_SNAKE_CASE__				)

     # load HuggingFace model
     __UpperCamelCase    							=BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__				) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE__				)
     model.eval()
     model.load_state_dict(SCREAMING_SNAKE_CASE__				)

     # Check outputs on an image
     __UpperCamelCase    							=BeitImageProcessor(
         size=config.image_size			,     resample=PILImageResampling.BILINEAR			,     do_center_crop=SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=prepare_img()

     __UpperCamelCase    							=image_processor(images=SCREAMING_SNAKE_CASE__			,     return_tensors='pt'				)
     __UpperCamelCase    							=encoding['pixel_values']

     __UpperCamelCase    							=model(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=outputs.logits

     # verify logits
     __UpperCamelCase    							=[1, 16] if 'rvlcdip' in checkpoint_url else [1, 1_96, 81_92]
     assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE__				), "Shape of logits not as expected"

     Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
     print(F'Saving model to {pytorch_dump_folder_path}'				)
     model.save_pretrained(SCREAMING_SNAKE_CASE__				)
     print(F'Saving image processor to {pytorch_dump_folder_path}'				)
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

     if push_to_hub:
          if has_lm_head:
               __UpperCamelCase    							='dit-base' if 'base' in checkpoint_url else 'dit-large'
          else:
               __UpperCamelCase    							='dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
          image_processor.push_to_hub(
              repo_path_or_name=Path(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)			,     organization='nielsr'			,     commit_message='Add image processor'			,     use_temp_dir=SCREAMING_SNAKE_CASE__			,     )
          model.push_to_hub(
              repo_path_or_name=Path(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)			,     organization='nielsr'			,     commit_message='Add model'			,     use_temp_dir=SCREAMING_SNAKE_CASE__			,     )


if __name__ == "__main__":
   _A		       =						argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_url',
       default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
       type=str,
       help='URL to the original PyTorch checkpoint (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
   )
   _A		       =						parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip_vision_model"






    def __init__(			self					,   A_=1408					,   A_=6144					,   A_=39					,   A_=16					,   A_=224					,   A_=14					,   A_="gelu"					,   A_=1E-6					,   A_=0.0					,   A_=1E-10					,   A_=True					,   **A_					,   )  ->       Tuple:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=qkv_bias






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls					,   'model_type'    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

         return cls.from_dict(A_					,   **A_    )






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  "instructblip_qformer"






    def __init__(			self					,   A_=30522					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=0.02					,   A_=1E-12					,   A_=0					,   A_="absolute"					,   A_=2					,   A_=1408					,   **A_					,   )  ->       Optional[Any]:
         super().__init__(pad_token_id=A_					,   **A_    )

         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=position_embedding_type
         __UpperCamelCase    							=cross_attention_frequency
         __UpperCamelCase    							=encoder_hidden_size






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=config_dict['qformer_config']

         if "model_type" in config_dict and hasattr(cls					,   'model_type'    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

         return cls.from_dict(A_					,   **A_    )






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip"
    UpperCAmelCase__						:		Optional[Any]      =  True






    def __init__(			self					,   A_=None					,   A_=None					,   A_=None					,   A_=32					,   **A_    )  ->       List[str]:
         super().__init__(**A_    )

         if vision_config is None:
              __UpperCamelCase    							={}
              logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'    )

         if qformer_config is None:
              __UpperCamelCase    							={}
              logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'    )

         if text_config is None:
              __UpperCamelCase    							={}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'    )

         __UpperCamelCase    							=InstructBlipVisionConfig(**A_    )
         __UpperCamelCase    							=InstructBlipQFormerConfig(**A_    )
         __UpperCamelCase    							=text_config['model_type'] if 'model_type' in text_config else 'opt'
         __UpperCamelCase    							=CONFIG_MAPPING[text_model_type](**A_    )

         __UpperCamelCase    							=self.text_config.tie_word_embeddings
         __UpperCamelCase    							=self.text_config.is_encoder_decoder

         __UpperCamelCase    							=num_query_tokens
         __UpperCamelCase    							=self.vision_config.hidden_size
         __UpperCamelCase    							=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         __UpperCamelCase    							=1.0
         __UpperCamelCase    							=0.02






    @classmethod
    def _a					(			cls					,   A_					,   A_					,   A_					,   **A_					,   )  ->       Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()					,   qformer_config=qformer_config.to_dict()					,   text_config=text_config.to_dict()					,   **A_					,   )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         __UpperCamelCase    							=self.vision_config.to_dict()
         __UpperCamelCase    							=self.qformer_config.to_dict()
         __UpperCamelCase    							=self.text_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output





