





"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_lowerCamelCase       =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase       =	'''cuda''' if torch.cuda.is_available() else '''cpu'''




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							str=100 ,      lowercase_   :							int=" "		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       text.split(lowercase_		)
 return [character.join(text[i : i + n]		).strip() for i in range(0 ,      len(lowercase_		) ,      lowercase_		)]




def 		lowerCAmelCase_	(  lowercase_   :							dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       [], []
 for title, text in zip(documents['''title'''] ,      documents['''text''']		):
  if text is not None:
   for passage in split_text(lowercase_		):
    titles.append(title if title is not None else ''''''		)
    texts.append(lowercase_		)
 return {"title": titles, "text": texts}




def 		lowerCAmelCase_	(  lowercase_   :							dict ,      lowercase_   :							DPRContextEncoder ,      lowercase_   :							DPRContextEncoderTokenizerFast		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       ctx_tokenizer(
     documents['''title'''] ,      documents['''text'''] ,      truncation=lowercase_ ,      padding='''longest''' ,      return_tensors='''pt'''		)['''input_ids''']
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       ctx_encoder(input_ids.to(device=lowercase_		) ,      return_dict=lowercase_		).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}




def 		lowerCAmelCase_	(  lowercase_   :							"RagExampleArguments" ,      lowercase_   :							"ProcessingArguments" ,      lowercase_   :							"IndexHnswArguments" ,      ):

 '''simple docstring'''






 logger.info('''Step 1 - Create the dataset'''		)
 ######################################

 # The dataset needed for RAG must have three columns:
 # - title (string): title of the document
 # - text (string): text of a passage of the document
 # - embeddings (array of dimension d): DPR representation of the passage

 # Let's say you have documents in tab-separated csv files with columns "title" and "text"
 assert os.path.isfile(rag_example_args.csv_path		), "Please provide a valid path to a csv file"

 # You can load a Dataset object this way
 __SCREAMING_SNAKE_CASE :			Any												=       load_dataset(
     '''csv''' ,      data_files=[rag_example_args.csv_path] ,      split='''train''' ,      delimiter='''\t''' ,      column_names=['''title''', '''text''']		)

 # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

 # Then split the documents into passages of 100 words
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       dataset.map(lowercase_ ,      batched=lowercase_ ,      num_proc=processing_args.num_proc		)

 # And compute the embeddings
 __SCREAMING_SNAKE_CASE :			str												=       DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		).to(device=lowercase_		)
 __SCREAMING_SNAKE_CASE :			Any												=       DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		)
 __SCREAMING_SNAKE_CASE :			Dict												=       Features(
     {'''text''': Value('''string'''		), '''title''': Value('''string'''		), '''embeddings''': Sequence(Value('''float32'''		)		)}		)  # optional, save as float32 instead of float64 to save space
 __SCREAMING_SNAKE_CASE :			List[str]												=       dataset.map(
     partial(lowercase_ ,      ctx_encoder=lowercase_ ,      ctx_tokenizer=lowercase_		) ,      batched=lowercase_ ,      batch_size=processing_args.batch_size ,      features=lowercase_ ,      )

 # And finally save your dataset
 __SCREAMING_SNAKE_CASE :			int												=       os.path.join(rag_example_args.output_dir ,      '''my_knowledge_dataset'''		)
 dataset.save_to_disk(lowercase_		)
 # from datasets import load_from_disk
 # dataset = load_from_disk(passages_path)  # to reload the dataset

 ######################################
 logger.info('''Step 2 - Index the dataset'''		)
 ######################################

 # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
 __SCREAMING_SNAKE_CASE :			Dict												=       faiss.IndexHNSWFlat(index_hnsw_args.d ,      index_hnsw_args.m ,      faiss.METRIC_INNER_PRODUCT		)
 dataset.add_faiss_index('''embeddings''' ,      custom_index=lowercase_		)

 # And save the index
 __SCREAMING_SNAKE_CASE :			str												=       os.path.join(rag_example_args.output_dir ,      '''my_knowledge_dataset_hnsw_index.faiss'''		)
 dataset.get_index('''embeddings'''		).save(lowercase_		)
 # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

 ######################################
 logger.info('''Step 3 - Load RAG'''		)
 ######################################

 # Easy way to load the model
 __SCREAMING_SNAKE_CASE :			int												=       RagRetriever.from_pretrained(
     rag_example_args.rag_model_name ,      index_name='''custom''' ,      indexed_dataset=lowercase_		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,      retriever=lowercase_		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       RagTokenizer.from_pretrained(rag_example_args.rag_model_name		)

 # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
 # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

 ######################################
 logger.info('''Step 4 - Have fun'''		)
 ######################################

 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       rag_example_args.question or '''What does Moses\' rod turn into ?'''
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tokenizer.question_encoder(lowercase_ ,      return_tensors='''pt'''		)['''input_ids''']
 __SCREAMING_SNAKE_CASE :			int												=       model.generate(lowercase_		)
 __SCREAMING_SNAKE_CASE :			str												=       tokenizer.batch_decode(lowercase_ ,      skip_special_tokens=lowercase_		)[0]
 logger.info('''Q: ''' + question		)
 logger.info('''A: ''' + generated_string		)





@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          default=str(Path(__UpperCAmelCase   ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''   ) ,     metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''} ,     )
      lowerCamelCase__						= field(
          default='''facebook/rag-sequence-nq''' ,     metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''} ,     )
      lowerCamelCase__						= field(
          default='''facebook/dpr-ctx_encoder-multiset-base''' ,     metadata={
              '''help''': (
                  '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
                  ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
              )
          } ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''} ,     )







@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={
              '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
          } ,     )
      lowerCamelCase__						= field(
          default=16 ,     metadata={
              '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
          } ,     )







@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          default=768 ,     metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''} ,     )
      lowerCamelCase__						= field(
          default=128 ,     metadata={
              '''help''': (
                  '''The number of bi-directional links created for every new element during the HNSW index construction.'''
              )
          } ,     )


if __name__ == "__main__":
    logging.basicConfig(level=logging.WARNING)
    logger.setLevel(logging.INFO)

    _lowerCamelCase       =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    _lowerCamelCase     ,		_lowerCamelCase     ,		_lowerCamelCase       =	parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        _lowerCamelCase       =	rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)








"""simple docstring"""





import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_lowerCamelCase       =	{
    '''vit_b32''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 32,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 5_12,
        '''image_resolution''': 7_68,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 12,
        '''vision_width''': 7_68,
        '''vision_patch_size''': 16,
        '''transformer_width''': 5_12,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 7_68,
        '''image_resolution''': 8_40,
        '''context_length''': 16,
        '''vocab_size''': 4_94_08,
        '''vision_layers''': 24,
        '''vision_width''': 10_24,
        '''vision_patch_size''': 14,
        '''transformer_width''': 7_68,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}




def 		lowerCAmelCase_	(  lowercase_   :							Tuple ,      lowercase_   :							List[Any]="" ,      lowercase_   :							List[Any]="/"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []

 for k, v in params.items():
  __SCREAMING_SNAKE_CASE :			List[str]												=       parent_key + sep + k if parent_key else k

  if isinstance(lowercase_ ,      collections.MutableMapping		):
   items.extend(flatten_nested_dict(lowercase_ ,      lowercase_ ,      sep=lowercase_		).items()		)
  else:
   items.append((new_key, v)		)
 return dict(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 return jax.tree_util.tree_map(lambda lowercase_		: x.astype(jnp.floataa		) if x.dtype == jnp.bfloataa else x ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_attn_layer.in_proj_weight.chunk(3 ,      dim=0		)
 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       pt_attn_layer.in_proj_bias.chunk(3 ,      dim=0		)

 __SCREAMING_SNAKE_CASE :			Any												=       pt_attn_layer.out_proj.weight
 __SCREAMING_SNAKE_CASE :			str												=       pt_attn_layer.out_proj.bias

 __SCREAMING_SNAKE_CASE :			Any												=       q_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       q_proj_bias

 __SCREAMING_SNAKE_CASE :			Any												=       k_proj
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       k_proj_bias

 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj
 __SCREAMING_SNAKE_CASE :			List[str]												=       v_proj_bias

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       out_proj_weights
 __SCREAMING_SNAKE_CASE :			int												=       out_proj_bias




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							Dict		):

 '''simple docstring'''






 copy_linear(hf_mlp.fca ,      pt_mlp.c_fc		)
 copy_linear(hf_mlp.fca ,      pt_mlp.c_proj		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_linear.weight
 __SCREAMING_SNAKE_CASE :			List[Any]												=       pt_linear.bias




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Any		):

 '''simple docstring'''






 copy_linear(hf_layer.layer_norma ,      pt_layer.ln_a		)
 copy_linear(hf_layer.layer_norma ,      pt_layer.ln_a		)

 # copy MLP
 copy_mlp(hf_layer.mlp ,      pt_layer.mlp		)

 # copy attn
 copy_attn_layer(hf_layer.self_attn ,      pt_layer.attn		)




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Any		):

 '''simple docstring'''






 for hf_layer, pt_layer in zip(lowercase_ ,      lowercase_		):
  copy_layer(lowercase_ ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.token_embedding.weight
 __SCREAMING_SNAKE_CASE :			str												=       pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm ,      pt_model.ln_final		)

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers ,      pt_model.transformer.resblocks		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model ,      lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm ,      pt_model.visual.ln_pre		)
 copy_linear(hf_model.vision_model.post_layernorm ,      pt_model.visual.ln_post		)

 # copy embeds
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.conva.weight.data
 __SCREAMING_SNAKE_CASE :			Tuple												=       pt_model.visual.class_embedding
 __SCREAMING_SNAKE_CASE :			Dict												=       pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers ,      pt_model.visual.transformer.resblocks		)




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']		)

 __SCREAMING_SNAKE_CASE :			Dict												=       torch.from_numpy(flax_class_token_params['''scale''']		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(flax_class_token_params['''bias''']		)
 __SCREAMING_SNAKE_CASE :			Dict												=       nn.Parameter(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Parameter(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			List[Any]												=       hf_model.state_dict()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

 # Rename class prediction head flax params to pytorch HF
 __SCREAMING_SNAKE_CASE :			Dict												=       flatten_nested_dict(flax_params['''class_head''']		)

 for flax_key, v in flax_class_params.items():
  __SCREAMING_SNAKE_CASE :			str												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Any												=       torch_key.replace('''Dense_0''' ,      '''dense0'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       '''class_head.''' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   __SCREAMING_SNAKE_CASE :			int												=       v.T

  __SCREAMING_SNAKE_CASE :			List[str]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

 # Rename box prediction box flax params to pytorch HF
 __SCREAMING_SNAKE_CASE :			Any												=       flatten_nested_dict(flax_params['''obj_box_head''']		)

 for flax_key, v in flax_box_params.items():
  __SCREAMING_SNAKE_CASE :			Tuple												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''_''' ,      ''''''		).lower()
  __SCREAMING_SNAKE_CASE :			List[str]												=       '''box_head.''' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   __SCREAMING_SNAKE_CASE :			Dict												=       v.T

  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       nn.Parameter(torch.from_numpy(lowercase_		)		)

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[Any] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 for k, v in flax_attn_params.items():
  if k.startswith('''transformer'''		):
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       k.replace('''transformer.resblocks''' ,      '''text_model.encoder.layers'''		)
  else:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       k.replace('''visual.transformer.resblocks''' ,      '''vision_model.encoder.layers'''		)

  __SCREAMING_SNAKE_CASE :			List[Any]												=       torch_key.replace('''attn''' ,      '''self_attn'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''key''' ,      '''k_proj'''		)
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch_key.replace('''value''' ,      '''v_proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''query''' ,      '''q_proj'''		)
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''out''' ,      '''out_proj'''		)

  if "bias" in torch_key and v.ndim == 2:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       v.shape[0] * v.shape[1]
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       v.reshape(lowercase_		)

  if "weight" in torch_key and "out" in torch_key:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       (v.shape[0] * v.shape[1], v.shape[2])
   __SCREAMING_SNAKE_CASE :			List[str]												=       v.reshape(lowercase_		).T

  if "weight" in torch_key and "out" not in torch_key:
   __SCREAMING_SNAKE_CASE :			Any												=       (v.shape[0], v.shape[1] * v.shape[2])
   __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       v.reshape(lowercase_		).T

  # Copy flax CLIP attn params to HF PyTorch params
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch.from_numpy(lowercase_		)
  hf_backbone.state_dict()[torch_key].copy_(lowercase_		)




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       {}
 __SCREAMING_SNAKE_CASE :			int												=       []

 for k, v in params.items():
  if "attn." in k:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       k[: k.rindex('''attn.'''		) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(lowercase_		)
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].shape[-1]
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       params[base + '''out.weight'''].reshape(lowercase_ ,      lowercase_		).T
   __SCREAMING_SNAKE_CASE :			Dict												=       params[base + '''out.bias''']
  else:
   __SCREAMING_SNAKE_CASE :			Any												=       v
 return new_params




def 		lowerCAmelCase_	(  lowercase_   :							str ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       CLIP(**lowercase_		)
 torch_model.eval()
 __SCREAMING_SNAKE_CASE :			List[str]												=       torch_model.state_dict()

 __SCREAMING_SNAKE_CASE :			str												=       flatten_nested_dict(flax_params['''backbone''']['''clip''']		)
 __SCREAMING_SNAKE_CASE :			str												=       {}

 for flax_key, v in flax_clip_params.items():
  __SCREAMING_SNAKE_CASE :			int												=       flax_key.replace('''/''' ,      '''.'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''text.token_embedding.embedding''' ,      '''token_embedding.kernel'''		)

  if (
      torch_key.startswith('''text.transformer'''		)
      or torch_key.startswith('''text.text_projection'''		)
      or torch_key.startswith('''text.ln_final'''		)
      or torch_key.startswith('''text.positional_embedding'''		)
  ):
   __SCREAMING_SNAKE_CASE :			List[str]												=       torch_key[5:]

  __SCREAMING_SNAKE_CASE :			Optional[int]												=       torch_key.replace('''text_projection.kernel''' ,      '''text_projection'''		)
  __SCREAMING_SNAKE_CASE :			str												=       torch_key.replace('''visual.proj.kernel''' ,      '''visual.proj'''		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       torch_key.replace('''.scale''' ,      '''.weight'''		)
  __SCREAMING_SNAKE_CASE :			Dict												=       torch_key.replace('''.kernel''' ,      '''.weight'''		)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       v.transpose(3 ,      2 ,      0 ,      1		)

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   __SCREAMING_SNAKE_CASE :			str												=       v.T

  __SCREAMING_SNAKE_CASE :			Any												=       v

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       _convert_attn_layers(lowercase_		)
 new_torch_params.update(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       {}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   __SCREAMING_SNAKE_CASE :			Any												=       torch.from_numpy(new_torch_params[name]		)
   torch_clip_params[name].copy_(lowercase_		)
  else:
   __SCREAMING_SNAKE_CASE :			Tuple												=       param

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		lowerCAmelCase_	(  lowercase_   :							Any ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							str ,      lowercase_   :							Optional[Any] ,      lowercase_   :							List[Any]=None		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       Repository(lowercase_ ,      clone_from=F'''google/{pytorch_dump_folder_path}'''		)
 repo.git_pull()

 if config_path is not None:
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       OwlViTConfig.from_pretrained(lowercase_		)
 else:
  __SCREAMING_SNAKE_CASE :			int												=       OwlViTConfig()

 __SCREAMING_SNAKE_CASE :			List[str]												=       OwlViTModel(lowercase_		).eval()
 __SCREAMING_SNAKE_CASE :			Tuple												=       OwlViTForObjectDetection(lowercase_		).eval()

 copy_text_model_and_projection(lowercase_ ,      lowercase_		)
 copy_vision_model_and_projection(lowercase_ ,      lowercase_		)
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       pt_backbone.logit_scale
 copy_flax_attn_params(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       hf_backbone
 copy_class_merge_token(lowercase_ ,      lowercase_		)
 copy_class_box_heads(lowercase_ ,      lowercase_		)

 # Save HF model
 hf_model.save_pretrained(repo.local_dir		)

 # Initialize image processor
 __SCREAMING_SNAKE_CASE :			List[Any]												=       OwlViTImageProcessor(
     size=config.vision_config.image_size ,      crop_size=config.vision_config.image_size		)
 # Initialize tokenizer
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' ,      pad_token='''!''' ,      model_max_length=16		)

 # Initialize processor
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       OwlViTProcessor(image_processor=lowercase_ ,      tokenizer=lowercase_		)
 image_processor.save_pretrained(repo.local_dir		)
 processor.save_pretrained(repo.local_dir		)

 repo.git_add()
 repo.git_commit('''Upload model and processor'''		)
 repo.git_push()


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--owlvit_version''',
        default=None,
        type=str,
        required=True,
        help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
    )
    parser.add_argument(
        '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
    )
    parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()

    # Initialize PyToch clip model
    _lowerCamelCase       =	args.owlvit_version
    if model_name == "clip_b16":
        _lowerCamelCase       =	CONFIGS['''vit_b16''']
    elif model_name == "clip_b32":
        _lowerCamelCase       =	CONFIGS['''vit_b32''']
    elif model_name == "clip_l14":
        _lowerCamelCase       =	CONFIGS['''vit_l14''']

    # Load from checkpoint and convert params to float-32
    _lowerCamelCase       =	checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
    _lowerCamelCase       =	jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
    del variables

    # Convert CLIP backbone
    _lowerCamelCase     ,		_lowerCamelCase     ,		_lowerCamelCase       =	convert_clip_backbone(flax_params, torch_config)

    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)

