
'''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,
)


A							     = {
    "vit_b32": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 3_2,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_b16": {
        "embed_dim": 5_1_2,
        "image_resolution": 7_6_8,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 1_2,
        "vision_width": 7_6_8,
        "vision_patch_size": 1_6,
        "transformer_width": 5_1_2,
        "transformer_heads": 8,
        "transformer_layers": 1_2,
    },
    "vit_l14": {
        "embed_dim": 7_6_8,
        "image_resolution": 8_4_0,
        "context_length": 1_6,
        "vocab_size": 4_9_4_0_8,
        "vision_layers": 2_4,
        "vision_width": 1_0_2_4,
        "vision_patch_size": 1_4,
        "transformer_width": 7_6_8,
        "transformer_heads": 1_2,
        "transformer_layers": 1_2,
    },
}
def 	snake_case_							(				_a  :				Any			,       _a  :				str=""			,       _a  :				Dict="/"      ):
      snake_case_    							=				[]

      for k, v in params.items():
            snake_case_    							=				parent_key + sep + k if parent_key else k

            if isinstance(_a			,       collections.MutableMapping      ):
                  items.extend(flatten_nested_dict(_a			,       _a			,       sep=_a      ).items()      )
            else:
                  items.append((new_key, v)      )
      return dict(_a      )
def 	snake_case_							(				_a  :				str      ):
      return jax.tree_util.tree_map(lambda _a      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x			,       _a      )
def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Optional[Any]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				pt_attn_layer.in_proj_weight.chunk(3			,       dim=0      )
      snake_case_							,						snake_case_							,						snake_case_    							=				pt_attn_layer.in_proj_bias.chunk(3			,       dim=0      )

      snake_case_    							=				pt_attn_layer.out_proj.weight
      snake_case_    							=				pt_attn_layer.out_proj.bias

      snake_case_    							=				q_proj
      snake_case_    							=				q_proj_bias

      snake_case_    							=				k_proj
      snake_case_    							=				k_proj_bias

      snake_case_    							=				v_proj
      snake_case_    							=				v_proj_bias

      snake_case_    							=				out_proj_weights
      snake_case_    							=				out_proj_bias
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      copy_linear(hf_mlp.fca			,       pt_mlp.c_fc      )
      copy_linear(hf_mlp.fca			,       pt_mlp.c_proj      )
def 	snake_case_							(				_a  :				Any			,       _a  :				List[str]      ):
      snake_case_    							=				pt_linear.weight
      snake_case_    							=				pt_linear.bias
def 	snake_case_							(				_a  :				List[str]			,       _a  :				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 	snake_case_							(				_a  :				List[str]			,       _a  :				str      ):
      for hf_layer, pt_layer in zip(_a			,       _a      ):
            copy_layer(_a			,       _a      )
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Dict      ):
      # copy  embeds
      snake_case_    							=				pt_model.token_embedding.weight
      snake_case_    							=				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 	snake_case_							(				_a  :				Tuple			,       _a  :				Optional[int]      ):
      # copy projection
      snake_case_    							=				pt_model.text_projection.data.T

      # copy text encoder
      copy_encoder(hf_model.text_model			,       _a      )
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int      ):
      # copy projection
      snake_case_    							=				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
      snake_case_    							=				pt_model.visual.conva.weight.data
      snake_case_    							=				pt_model.visual.class_embedding
      snake_case_    							=				pt_model.visual.positional_embedding.data

      # copy encoder
      copy_layers(hf_model.vision_model.encoder.layers			,       pt_model.visual.transformer.resblocks      )
def 	snake_case_							(				_a  :				Any			,       _a  :				Dict      ):
      snake_case_    							=				flatten_nested_dict(flax_params["backbone"]["merged_class_token"]      )

      snake_case_    							=				torch.from_numpy(flax_class_token_params["scale"]      )
      snake_case_    							=				torch.from_numpy(flax_class_token_params["bias"]      )
      snake_case_    							=				nn.Parameter(_a      )
      snake_case_    							=				nn.Parameter(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Tuple      ):
      snake_case_    							=				hf_model.state_dict()
      snake_case_    							=				{}

      # Rename class prediction head flax params to pytorch HF
      snake_case_    							=				flatten_nested_dict(flax_params["class_head"]      )

      for flax_key, v in flax_class_params.items():
            snake_case_    							=				flax_key.replace("/"			,       "."      )
            snake_case_    							=				torch_key.replace(".kernel"			,       ".weight"      )
            snake_case_    							=				torch_key.replace("Dense_0"			,       "dense0"      )
            snake_case_    							=				"class_head." + torch_key

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

            snake_case_    							=				nn.Parameter(torch.from_numpy(_a      )      )

      # Rename box prediction box flax params to pytorch HF
      snake_case_    							=				flatten_nested_dict(flax_params["obj_box_head"]      )

      for flax_key, v in flax_box_params.items():
            snake_case_    							=				flax_key.replace("/"			,       "."      )
            snake_case_    							=				torch_key.replace(".kernel"			,       ".weight"      )
            snake_case_    							=				torch_key.replace("_"			,       ""      ).lower()
            snake_case_    							=				"box_head." + torch_key

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

            snake_case_    							=				nn.Parameter(torch.from_numpy(_a      )      )

      # Copy flax params to PyTorch params
      for name, param in new_params.items():
            if name in pt_params.keys():
                  pt_params[name].copy_(_a      )
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Optional[int]      ):
      for k, v in flax_attn_params.items():
            if k.startswith("transformer"      ):
                  snake_case_    							=				k.replace("transformer.resblocks"			,       "text_model.encoder.layers"      )
            else:
                  snake_case_    							=				k.replace("visual.transformer.resblocks"			,       "vision_model.encoder.layers"      )

            snake_case_    							=				torch_key.replace("attn"			,       "self_attn"      )
            snake_case_    							=				torch_key.replace("key"			,       "k_proj"      )
            snake_case_    							=				torch_key.replace("value"			,       "v_proj"      )
            snake_case_    							=				torch_key.replace("query"			,       "q_proj"      )
            snake_case_    							=				torch_key.replace("out"			,       "out_proj"      )

            if "bias" in torch_key and v.ndim == 2:
                  snake_case_    							=				v.shape[0] * v.shape[1]
                  snake_case_    							=				v.reshape(_a      )

            if "weight" in torch_key and "out" in torch_key:
                  snake_case_    							=				(v.shape[0] * v.shape[1], v.shape[2])
                  snake_case_    							=				v.reshape(_a      ).T

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

            # Copy flax CLIP attn params to HF PyTorch params
            snake_case_    							=				torch.from_numpy(_a      )
            hf_backbone.state_dict()[torch_key].copy_(_a      )
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				{}
      snake_case_    							=				[]

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

                  processed_attn_layers.append(_a      )
                  snake_case_    							=				params[base + "out.weight"].shape[-1]
                  snake_case_    							=				params[base + "out.weight"].reshape(_a			,       _a      ).T
                  snake_case_    							=				params[base + "out.bias"]
            else:
                  snake_case_    							=				v
      return new_params
def 	snake_case_							(				_a  :				Any			,       _a  :				Any      ):
      snake_case_    							=				CLIP(**_a      )
      torch_model.eval()
      snake_case_    							=				torch_model.state_dict()

      snake_case_    							=				flatten_nested_dict(flax_params["backbone"]["clip"]      )
      snake_case_    							=				{}

      for flax_key, v in flax_clip_params.items():
            snake_case_    							=				flax_key.replace("/"			,       "."      )
            snake_case_    							=				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"      )
            ):
                  snake_case_    							=				torch_key[5:]

            snake_case_    							=				torch_key.replace("text_projection.kernel"			,       "text_projection"      )
            snake_case_    							=				torch_key.replace("visual.proj.kernel"			,       "visual.proj"      )
            snake_case_    							=				torch_key.replace(".scale"			,       ".weight"      )
            snake_case_    							=				torch_key.replace(".kernel"			,       ".weight"      )

            if "conv" in torch_key or "downsample.0.weight" in torch_key:
                  snake_case_    							=				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
                  snake_case_    							=				v.T

            snake_case_    							=				v

      snake_case_    							=				_convert_attn_layers(_a      )
      new_torch_params.update(_a      )
      snake_case_    							=				{}

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

      return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				int			,       _a  :				int			,       _a  :				Dict			,       _a  :				str=None      ):
      snake_case_    							=				Repository(_a			,       clone_from=F'''google/{pytorch_dump_folder_path}'''      )
      repo.git_pull()

      if config_path is not None:
            snake_case_    							=				OwlViTConfig.from_pretrained(_a      )
      else:
            snake_case_    							=				OwlViTConfig()

      snake_case_    							=				OwlViTModel(_a      ).eval()
      snake_case_    							=				OwlViTForObjectDetection(_a      ).eval()

      copy_text_model_and_projection(_a			,       _a      )
      copy_vision_model_and_projection(_a			,       _a      )
      snake_case_    							=				pt_backbone.logit_scale
      copy_flax_attn_params(_a			,       _a      )

      snake_case_    							=				hf_backbone
      copy_class_merge_token(_a			,       _a      )
      copy_class_box_heads(_a			,       _a      )

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

      # Initialize image processor
      snake_case_    							=				OwlViTImageProcessor(
          size=config.vision_config.image_size			,       crop_size=config.vision_config.image_size      )
      # Initialize tokenizer
      snake_case_    							=				CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"			,       pad_token="!"			,       model_max_length=16      )

      # Initialize processor
      snake_case_    							=				OwlViTProcessor(image_processor=_a			,       tokenizer=_a      )
      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__":
     A							     = 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."
     )
     A							     = parser.parse_args()

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

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

     # Convert CLIP backbone
     A     ,  A     ,  A							     = 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"""






from sklearn.metrics import precision_score

import datasets


__lowerCAmelCase				      =   '''
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
'''


__lowerCAmelCase				      =   '''
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - \'warn\': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

    Example 1-A simple binary example
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'precision\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'precision\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric("precision")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(results)
        {\'precision\': 0.23529411764705882}

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'precision\': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'precision\': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results[\'precision\']])
        [0.67, 0.0, 0.0]
'''


__lowerCAmelCase				      =   '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,       _KWARGS_DESCRIPTION       )
class 	_lowerCAmelCase    (      datasets.Metric       ):

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

									"""simple docstring"""
									return datasets.MetricInfo(
									    description=_DESCRIPTION ,		citation=_CITATION ,		inputs_description=_KWARGS_DESCRIPTION ,		features=datasets.Features(
									        {
									            '''predictions''': datasets.Sequence(datasets.Value('''int32'''    )    ),
									            '''references''': datasets.Sequence(datasets.Value('''int32'''    )    ),
									        }
									        if self.config_name == '''multilabel'''
									        else {
									            '''predictions''': datasets.Value('''int32'''    ),
									            '''references''': datasets.Value('''int32'''    ),
									        }    ) ,		reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html'''] ,		)

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Any ,		a       :    str ,		a       :    str=None ,		a       :    Optional[Any]=1 ,		a       :    Any="binary" ,		a       :    List[Any]=None ,		a       :    Union[str, Any]="warn" ,		)   ->    Any:

									"""simple docstring"""
									lowercase										=							precision_score(
									    a ,		a ,		labels=a ,		pos_label=a ,		average=a ,		sample_weight=a ,		zero_division=a ,		)
									return {"precision": float(a    ) if score.size == 1 else score}