

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''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49408,
        '''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''': 49408,
        '''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''': 49408,
        '''vision_layers''': 24,
        '''vision_width''': 1024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}



def    UpperCamelCase(					lowercase_			,					lowercase_=""			,					lowercase_="/"							)	->				Tuple:





  '''simple docstring'''





  snake_case_       						=      []

  for k, v in params.items():
    snake_case_       						=      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    UpperCamelCase(					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[Any]:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Any:





  '''simple docstring'''





  snake_case_       						=      pt_linear.weight
  snake_case_       						=      pt_linear.bias



def    UpperCamelCase(					lowercase_			,					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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				str:





  '''simple docstring'''





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



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      pt_model.text_projection.data.T

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



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[Any]:





  '''simple docstring'''





  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(lowercase_							)
  snake_case_       						=      nn.Parameter(lowercase_							)



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[Any]:





  '''simple docstring'''





  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(lowercase_							)							)

  # 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(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    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Tuple:





  '''simple docstring'''





  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(lowercase_							)

    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(lowercase_							).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(lowercase_							).T

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



def    UpperCamelCase(					lowercase_							)	->				int:





  '''simple docstring'''





  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(lowercase_							)
      snake_case_       						=      params[base + """out.weight"""].shape[-1]
      snake_case_       						=      params[base + """out.weight"""].reshape(lowercase_			,					lowercase_							).T
      snake_case_       						=      params[base + """out.bias"""]
    else:
      snake_case_       						=      v
  return new_params



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      CLIP(**lowercase_							)
  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(lowercase_							)
  new_torch_params.update(lowercase_							)
  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_(lowercase_							)
    else:
      snake_case_       						=      param

  return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_=None							)	->				int:





  '''simple docstring'''





  snake_case_       						=      Repository(lowercase_			,					clone_from=f'''google/{pytorch_dump_folder_path}'''							)
  repo.git_pull()

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

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

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

  snake_case_       						=      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
  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=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)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)