






"""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,
)


_UpperCamelCase   :						List[str]     						= {
    """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 				a (     UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				List[str]=""     ,	UpperCamelCase_							:				Tuple="/"		)  ->		Optional[int]:
 __A	:       int       =	[]

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

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




def 				a (     UpperCamelCase_							:				Tuple		)  ->		Optional[Any]:
 return jax.tree_util.tree_map(lambda UpperCamelCase_		: x.astype(jnp.floataa		) if x.dtype == jnp.bfloataa else x     ,	UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				int     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[Any]:
 __A	:       Any       =	pt_attn_layer.in_proj_weight.chunk(3     ,	dim=0		)
 __A	:       Optional[Any]       =	pt_attn_layer.in_proj_bias.chunk(3     ,	dim=0		)

 __A	:       Tuple       =	pt_attn_layer.out_proj.weight
 __A	:       Dict       =	pt_attn_layer.out_proj.bias

 __A	:       str       =	q_proj
 __A	:       Dict       =	q_proj_bias

 __A	:       Optional[Any]       =	k_proj
 __A	:       Union[str, Any]       =	k_proj_bias

 __A	:       Union[str, Any]       =	v_proj
 __A	:       str       =	v_proj_bias

 __A	:       Tuple       =	out_proj_weights
 __A	:       Any       =	out_proj_bias




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Tuple		)  ->		Dict:
 copy_linear(hf_mlp.fca     ,	pt_mlp.c_fc		)
 copy_linear(hf_mlp.fca     ,	pt_mlp.c_proj		)




def 				a (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Union[str, Any]		)  ->		str:
 __A	:       Dict       =	pt_linear.weight
 __A	:       Optional[int]       =	pt_linear.bias




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				int		)  ->		int:
 # copy layer norms
 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 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[int]:
 for hf_layer, pt_layer in zip(UpperCamelCase_     ,	UpperCamelCase_		):
  copy_layer(UpperCamelCase_     ,	UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				str		)  ->		Any:
 # copy  embeds
 __A	:       Optional[int]       =	pt_model.token_embedding.weight
 __A	:       Tuple       =	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 				a (     UpperCamelCase_							:				int     ,	UpperCamelCase_							:				List[str]		)  ->		Optional[int]:
 # copy projection
 __A	:       Optional[int]       =	pt_model.text_projection.data.T

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




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				Dict		)  ->		Union[str, Any]:
 # copy projection
 __A	:       Tuple       =	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
 __A	:       int       =	pt_model.visual.conva.weight.data
 __A	:       str       =	pt_model.visual.class_embedding
 __A	:       List[Any]       =	pt_model.visual.positional_embedding.data

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




def 				a (     UpperCamelCase_							:				List[Any]     ,	UpperCamelCase_							:				Any		)  ->		Optional[Any]:
 __A	:       Tuple       =	flatten_nested_dict(flax_params['backbone']['merged_class_token']		)

 __A	:       Optional[Any]       =	torch.from_numpy(flax_class_token_params['scale']		)
 __A	:       str       =	torch.from_numpy(flax_class_token_params['bias']		)
 __A	:       Any       =	nn.Parameter(UpperCamelCase_		)
 __A	:       Union[str, Any]       =	nn.Parameter(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				Optional[int]		)  ->		Optional[int]:
 __A	:       Optional[Any]       =	hf_model.state_dict()
 __A	:       List[str]       =	{}

 # Rename class prediction head flax params to pytorch HF
 __A	:       Union[str, Any]       =	flatten_nested_dict(flax_params['class_head']		)

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

  if "weight" in torch_key and v.ndim == 2:
   __A	:       Optional[Any]       =	v.T

  __A	:       List[Any]       =	nn.Parameter(torch.from_numpy(UpperCamelCase_		)		)

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

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

  if "weight" in torch_key and v.ndim == 2:
   __A	:       Optional[int]       =	v.T

  __A	:       List[str]       =	nn.Parameter(torch.from_numpy(UpperCamelCase_		)		)

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




def 				a (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Tuple		)  ->		Tuple:
 for k, v in flax_attn_params.items():
  if k.startswith('transformer'		):
   __A	:       Optional[Any]       =	k.replace('transformer.resblocks'     ,	'text_model.encoder.layers'		)
  else:
   __A	:       str       =	k.replace('visual.transformer.resblocks'     ,	'vision_model.encoder.layers'		)

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

  if "bias" in torch_key and v.ndim == 2:
   __A	:       str       =	v.shape[0] * v.shape[1]
   __A	:       Optional[int]       =	v.reshape(UpperCamelCase_		)

  if "weight" in torch_key and "out" in torch_key:
   __A	:       Optional[Any]       =	(v.shape[0] * v.shape[1], v.shape[2])
   __A	:       Dict       =	v.reshape(UpperCamelCase_		).T

  if "weight" in torch_key and "out" not in torch_key:
   __A	:       Optional[Any]       =	(v.shape[0], v.shape[1] * v.shape[2])
   __A	:       Optional[Any]       =	v.reshape(UpperCamelCase_		).T

  # Copy flax CLIP attn params to HF PyTorch params
  __A	:       Union[str, Any]       =	torch.from_numpy(UpperCamelCase_		)
  hf_backbone.state_dict()[torch_key].copy_(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Optional[Any]		)  ->		Tuple:
 __A	:       Tuple       =	{}
 __A	:       Optional[Any]       =	[]

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

   processed_attn_layers.append(UpperCamelCase_		)
   __A	:       List[Any]       =	params[base + 'out.weight'].shape[-1]
   __A	:       Tuple       =	params[base + 'out.weight'].reshape(UpperCamelCase_     ,	UpperCamelCase_		).T
   __A	:       Any       =	params[base + 'out.bias']
  else:
   __A	:       Optional[int]       =	v
 return new_params




def 				a (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Optional[int]		)  ->		List[str]:
 __A	:       Dict       =	CLIP(**UpperCamelCase_		)
 torch_model.eval()
 __A	:       Any       =	torch_model.state_dict()

 __A	:       List[str]       =	flatten_nested_dict(flax_params['backbone']['clip']		)
 __A	:       Optional[int]       =	{}

 for flax_key, v in flax_clip_params.items():
  __A	:       Any       =	flax_key.replace('/'     ,	'.'		)
  __A	:       Any       =	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'		)
  ):
   __A	:       Optional[int]       =	torch_key[5:]

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

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   __A	:       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
   __A	:       Optional[Any]       =	v.T

  __A	:       Any       =	v

 __A	:       Any       =	_convert_attn_layers(UpperCamelCase_		)
 new_torch_params.update(UpperCamelCase_		)
 __A	:       Optional[Any]       =	{}

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

 return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 				a (     UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Tuple=None		)  ->		List[Any]:
 __A	:       int       =	Repository(UpperCamelCase_     ,	clone_from=F"google/{pytorch_dump_folder_path}"		)
 repo.git_pull()

 if config_path is not None:
  __A	:       Optional[int]       =	OwlViTConfig.from_pretrained(UpperCamelCase_		)
 else:
  __A	:       Dict       =	OwlViTConfig()

 __A	:       int       =	OwlViTModel(UpperCamelCase_		).eval()
 __A	:       Union[str, Any]       =	OwlViTForObjectDetection(UpperCamelCase_		).eval()

 copy_text_model_and_projection(UpperCamelCase_     ,	UpperCamelCase_		)
 copy_vision_model_and_projection(UpperCamelCase_     ,	UpperCamelCase_		)
 __A	:       Tuple       =	pt_backbone.logit_scale
 copy_flax_attn_params(UpperCamelCase_     ,	UpperCamelCase_		)

 __A	:       str       =	hf_backbone
 copy_class_merge_token(UpperCamelCase_     ,	UpperCamelCase_		)
 copy_class_box_heads(UpperCamelCase_     ,	UpperCamelCase_		)

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

 # Initialize image processor
 __A	:       str       =	OwlViTImageProcessor(
     size=config.vision_config.image_size     ,	crop_size=config.vision_config.image_size		)
 # Initialize tokenizer
 __A	:       List[str]       =	CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     ,	pad_token='!'     ,	model_max_length=1_6		)

 # Initialize processor
 __A	:       List[str]       =	OwlViTProcessor(image_processor=UpperCamelCase_     ,	tokenizer=UpperCamelCase_		)
 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__":
   _UpperCamelCase   :						Dict     						= 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."""
   )
   _UpperCamelCase   :						int     						= parser.parse_args()

   # Initialize PyToch clip model
   _UpperCamelCase   :						Union[str, Any]     						= args.owlvit_version
   if model_name == "clip_b16":
      _UpperCamelCase   :						List[Any]     						= CONFIGS["""vit_b16"""]
   elif model_name == "clip_b32":
      _UpperCamelCase   :						Union[str, Any]     						= CONFIGS["""vit_b32"""]
   elif model_name == "clip_l14":
      _UpperCamelCase   :						Optional[int]     						= CONFIGS["""vit_l14"""]

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

   # Convert CLIP backbone
   _UpperCamelCase   :						int     						= convert_clip_backbone(flax_params, torch_config)

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













"""simple docstring"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_UpperCamelCase   :						List[Any]     						= """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_UpperCamelCase   :						Any     						= """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_UpperCamelCase   :						Dict     						= """"""

# TODO: Add the licence for the dataset here if you can find it
_UpperCamelCase   :						Tuple     						= """"""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_UpperCamelCase   :						str     						= {
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}






class __a   (   datasets.GeneratorBasedBuilder				):
  __lowercase    :    int						=					datasets.Version('1.1.0'				)

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  __lowercase    :    Optional[Any]						=					[
      datasets.BuilderConfig(name='first_domain' ,version=snake_case_ ,description='This part of my dataset covers a first domain'				),
      datasets.BuilderConfig(name='second_domain' ,version=snake_case_ ,description='This part of my dataset covers a second domain'				),
  ]

  __lowercase    :    Any						=					'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.



  def 						A_				(self  :		Optional[Any]   )		->							List[Any]:
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    __A	:       Dict       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option1': datasets.Value('string'   ),
            'answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    __A	:       Optional[int]       =	datasets.Features(
        {
            'sentence': datasets.Value('string'   ),
            'option2': datasets.Value('string'   ),
            'second_domain_answer': datasets.Value('string'   )
            # These are the features of your dataset like images, labels ...
        }   )
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION				,			features=lowercase_				,			homepage=_HOMEPAGE				,			license=_LICENSE				,			citation=_CITATION				,			)



  def 						A_				(self  :		Dict				,			lowercase_  :		Union[str, Any]   )		->							Union[str, Any]:
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   __A	:       Optional[int]       =	_URLS[self.config.name]
   __A	:       Any       =	dl_manager.download_and_extract(lowercase_   )
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'train.jsonl'   ),
               'split': 'train',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'dev.jsonl'   ),
               'split': 'dev',
           }				,			),
       datasets.SplitGenerator(
           name=datasets.Split.TEST				,			gen_kwargs={
               'filepath': os.path.join(lowercase_				,			'test.jsonl'   ),
               'split': 'test'
           }				,			),
   ]



  def 						A_				(self  :		Tuple				,			lowercase_  :		Dict				,			lowercase_  :		Optional[Any]   )		->							Tuple:
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(lowercase_				,			encoding='utf-8'   ) as f:
    for key, row in enumerate(lowercase_   ):
     __A	:       int       =	json.loads(lowercase_   )
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }





