


"""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,
)


SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= {
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 24,
        "vision_width": 1024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=""       ,   UpperCAmelCase__="/"				)     ->     Optional[Any]:
   a   :	str						=    []

   for k, v in params.items():
      a   :	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__				)     ->     Optional[int]:
   return jax.tree_util.tree_map(lambda UpperCAmelCase__				: x.astype(jnp.floataa				) if x.dtype == jnp.bfloataa else x       ,   UpperCAmelCase__				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[int]:
   a	, a	, a   :	Optional[Any]						=    pt_attn_layer.in_proj_weight.chunk(3       ,   dim=0				)
   a	, a	, a   :	Any						=    pt_attn_layer.in_proj_bias.chunk(3       ,   dim=0				)

   a   :	Tuple						=    pt_attn_layer.out_proj.weight
   a   :	int						=    pt_attn_layer.out_proj.bias

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

   a   :	Any						=    k_proj
   a   :	Optional[Any]						=    k_proj_bias

   a   :	Optional[int]						=    v_proj
   a   :	Optional[int]						=    v_proj_bias

   a   :	List[str]						=    out_proj_weights
   a   :	Any						=    out_proj_bias




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   copy_linear(hf_mlp.fca       ,   pt_mlp.c_fc				)
   copy_linear(hf_mlp.fca       ,   pt_mlp.c_proj				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Any:
   a   :	Union[str, Any]						=    pt_linear.weight
   a   :	int						=    pt_linear.bias




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Union[str, Any]:
   # 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__       ,   UpperCAmelCase__				)     ->     List[str]:
   for hf_layer, pt_layer in zip(UpperCAmelCase__       ,   UpperCAmelCase__				):
      copy_layer(UpperCAmelCase__       ,   UpperCAmelCase__				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   # copy  embeds
   a   :	Dict						=    pt_model.token_embedding.weight
   a   :	Dict						=    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__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   # copy projection
   a   :	List[Any]						=    pt_model.text_projection.data.T

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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     str:
   # copy projection
   a   :	Dict						=    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   :	Optional[int]						=    pt_model.visual.class_embedding
   a   :	str						=    pt_model.visual.positional_embedding.data

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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Dict:
   a   :	List[str]						=    flatten_nested_dict(flax_params['backbone']['merged_class_token']				)

   a   :	Tuple						=    torch.from_numpy(flax_class_token_params['scale']				)
   a   :	Dict						=    torch.from_numpy(flax_class_token_params['bias']				)
   a   :	Dict						=    nn.Parameter(UpperCAmelCase__				)
   a   :	Tuple						=    nn.Parameter(UpperCAmelCase__				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Dict:
   a   :	Optional[Any]						=    hf_model.state_dict()
   a   :	Dict						=    {}

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

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

      if "weight" in torch_key and v.ndim == 2:
         a   :	str						=    v.T

      a   :	Union[str, Any]						=    nn.Parameter(torch.from_numpy(UpperCAmelCase__				)				)

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

   for flax_key, v in flax_box_params.items():
      a   :	int						=    flax_key.replace('/'       ,   '.'				)
      a   :	Dict						=    torch_key.replace('.kernel'       ,   '.weight'				)
      a   :	Any						=    torch_key.replace('_'       ,   ''				).lower()
      a   :	Optional[Any]						=    'box_head.' + torch_key

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

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

      a   :	Union[str, Any]						=    torch_key.replace('attn'       ,   'self_attn'				)
      a   :	Union[str, Any]						=    torch_key.replace('key'       ,   'k_proj'				)
      a   :	Tuple						=    torch_key.replace('value'       ,   'v_proj'				)
      a   :	List[Any]						=    torch_key.replace('query'       ,   'q_proj'				)
      a   :	List[str]						=    torch_key.replace('out'       ,   'out_proj'				)

      if "bias" in torch_key and v.ndim == 2:
         a   :	Optional[int]						=    v.shape[0] * v.shape[1]
         a   :	List[Any]						=    v.reshape(UpperCAmelCase__				)

      if "weight" in torch_key and "out" in torch_key:
         a   :	Optional[int]						=    (v.shape[0] * v.shape[1], v.shape[2])
         a   :	Union[str, Any]						=    v.reshape(UpperCAmelCase__				).T

      if "weight" in torch_key and "out" not in torch_key:
         a   :	int						=    (v.shape[0], v.shape[1] * v.shape[2])
         a   :	List[str]						=    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[int]:
   a   :	Dict						=    {}
   a   :	str						=    []

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

         processed_attn_layers.append(UpperCAmelCase__				)
         a   :	Union[str, Any]						=    params[base + 'out.weight'].shape[-1]
         a   :	List[str]						=    params[base + 'out.weight'].reshape(UpperCAmelCase__       ,   UpperCAmelCase__				).T
         a   :	List[Any]						=    params[base + 'out.bias']
      else:
         a   :	Tuple						=    v
   return new_params




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     int:
   a   :	Tuple						=    CLIP(**UpperCAmelCase__				)
   torch_model.eval()
   a   :	Tuple						=    torch_model.state_dict()

   a   :	Union[str, Any]						=    flatten_nested_dict(flax_params['backbone']['clip']				)
   a   :	Optional[Any]						=    {}

   for flax_key, v in flax_clip_params.items():
      a   :	Dict						=    flax_key.replace('/'       ,   '.'				)
      a   :	Union[str, 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   :	Tuple						=    torch_key[5:]

      a   :	Optional[Any]						=    torch_key.replace('text_projection.kernel'       ,   'text_projection'				)
      a   :	int						=    torch_key.replace('visual.proj.kernel'       ,   'visual.proj'				)
      a   :	Optional[Any]						=    torch_key.replace('.scale'       ,   '.weight'				)
      a   :	Optional[Any]						=    torch_key.replace('.kernel'       ,   '.weight'				)

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

      a   :	Optional[Any]						=    v

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

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

   return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=None				)     ->     Optional[int]:
   a   :	Union[str, Any]						=    Repository(UpperCAmelCase__       ,   clone_from=F'google/{pytorch_dump_folder_path}'				)
   repo.git_pull()

   if config_path is not None:
      a   :	Union[str, Any]						=    OwlViTConfig.from_pretrained(UpperCAmelCase__				)
   else:
      a   :	str						=    OwlViTConfig()

   a   :	str						=    OwlViTModel(UpperCAmelCase__				).eval()
   a   :	Optional[int]						=    OwlViTForObjectDetection(UpperCAmelCase__				).eval()

   copy_text_model_and_projection(UpperCAmelCase__       ,   UpperCAmelCase__				)
   copy_vision_model_and_projection(UpperCAmelCase__       ,   UpperCAmelCase__				)
   a   :	Optional[Any]						=    pt_backbone.logit_scale
   copy_flax_attn_params(UpperCAmelCase__       ,   UpperCAmelCase__				)

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

   # Initialize processor
   a   :	int						=    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__":
 SCREAMING_SNAKE_CASE__				:  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."
 )
 SCREAMING_SNAKE_CASE__				:  Dict						= parser.parse_args()

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

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

 # Convert CLIP backbone
 SCREAMING_SNAKE_CASE__			,					SCREAMING_SNAKE_CASE__			,					SCREAMING_SNAKE_CASE__				:  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 importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

SCREAMING_SNAKE_CASE__				:  str						= "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

SCREAMING_SNAKE_CASE__				:  Any						= "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION   )
class 	A_       (     datasets.Metric   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION  ,					citation=_CITATION  ,					homepage='https://github.com/chakki-works/seqeval'  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
             {
                 'predictions': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
                 'references': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
             }						)  ,					codebase_urls=['https://github.com/chakki-works/seqeval']  ,					reference_urls=['https://github.com/chakki-works/seqeval']  ,					)






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    classification_report(
         y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase  ,					suffix=__UpperCAmelCase  ,					output_dict=__UpperCAmelCase  ,					scheme=__UpperCAmelCase  ,					mode=__UpperCAmelCase  ,					sample_weight=__UpperCAmelCase  ,					zero_division=__UpperCAmelCase  ,					)
     report.pop('macro avg'						)
     report.pop('weighted avg'						)
     a   :	Optional[Any]						=    report.pop('micro avg'						)

     a   :	Optional[int]						=    {
         type_name: {
             'precision': score['precision'],
             'recall': score['recall'],
             'f1': score['f1-score'],
             'number': score['support'],
         }
         for type_name, score in report.items()
     }
     a   :	int						=    overall_score['precision']
     a   :	int						=    overall_score['recall']
     a   :	int						=    overall_score['f1-score']
     a   :	List[str]						=    accuracy_score(y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase						)

     return scores





