'''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''': 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 					UpperCAmelCase__      (							_lowerCAmelCase						: Dict,			_lowerCAmelCase						: Optional[int]="",			_lowerCAmelCase						: List[str]="/")						->		str:



      """simple docstring"""

      snake_case   :					Tuple							=						[]

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

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any])						->		Optional[Any]:



      """simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: int)						->		int:



      """simple docstring"""

      snake_case							,				snake_case							,				snake_case   :					Tuple							=						pt_attn_layer.in_proj_weight.chunk(3,			dim=0)
      snake_case							,				snake_case							,				snake_case   :					int							=						pt_attn_layer.in_proj_bias.chunk(3,			dim=0)

      snake_case   :					Tuple							=						pt_attn_layer.out_proj.weight
      snake_case   :					str							=						pt_attn_layer.out_proj.bias

      snake_case   :					int							=						q_proj
      snake_case   :					Optional[Any]							=						q_proj_bias

      snake_case   :					str							=						k_proj
      snake_case   :					Optional[int]							=						k_proj_bias

      snake_case   :					Tuple							=						v_proj
      snake_case   :					List[Any]							=						v_proj_bias

      snake_case   :					Tuple							=						out_proj_weights
      snake_case   :					List[Any]							=						out_proj_bias


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Dict,			_lowerCAmelCase						: Union[str, Any])						->		int:



      """simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: str)						->		int:



      """simple docstring"""

      snake_case   :					Dict							=						pt_linear.weight
      snake_case   :					Optional[Any]							=						pt_linear.bias


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: Tuple)						->		Optional[int]:



      """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__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: Tuple)						->		Any:



      """simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: int)						->		int:



      """simple docstring"""

      snake_case   :					Union[str, Any]							=						pt_model.token_embedding.weight
      snake_case   :					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 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: Optional[int])						->		Optional[int]:



      """simple docstring"""

      snake_case   :					int							=						pt_model.text_projection.data.T

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: Dict)						->		Any:



      """simple docstring"""

      snake_case   :					Optional[Any]							=						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   :					List[str]							=						pt_model.visual.conva.weight.data
      snake_case   :					Optional[Any]							=						pt_model.visual.class_embedding
      snake_case   :					Tuple							=						pt_model.visual.positional_embedding.data

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: List[Any])						->		Any:



      """simple docstring"""

      snake_case   :					Dict							=						flatten_nested_dict(flax_params["backbone"]["merged_class_token"])

      snake_case   :					str							=						torch.from_numpy(flax_class_token_params["scale"])
      snake_case   :					Union[str, Any]							=						torch.from_numpy(flax_class_token_params["bias"])
      snake_case   :					int							=						nn.Parameter(snake_case__)
      snake_case   :					List[Any]							=						nn.Parameter(snake_case__)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Any,			_lowerCAmelCase						: str)						->		Dict:



      """simple docstring"""

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

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

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

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

            snake_case   :					Tuple							=						nn.Parameter(torch.from_numpy(snake_case__))

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

      for flax_key, v in flax_box_params.items():
            snake_case   :					Dict							=						flax_key.replace("/",			".")
            snake_case   :					Tuple							=						torch_key.replace(".kernel",			".weight")
            snake_case   :					int							=						torch_key.replace("_",			"").lower()
            snake_case   :					List[Any]							=						"box_head." + torch_key

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

            snake_case   :					Optional[Any]							=						nn.Parameter(torch.from_numpy(snake_case__))

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any],			_lowerCAmelCase						: List[str])						->		Dict:



      """simple docstring"""

      for k, v in flax_attn_params.items():
            if k.startswith("transformer"):
                  snake_case   :					Tuple							=						k.replace("transformer.resblocks",			"text_model.encoder.layers")
            else:
                  snake_case   :					Any							=						k.replace("visual.transformer.resblocks",			"vision_model.encoder.layers")

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

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

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

            if "weight" in torch_key and "out" not in torch_key:
                  snake_case   :					Dict							=						(v.shape[0], v.shape[1] * v.shape[2])
                  snake_case   :					List[Any]							=						v.reshape(snake_case__).T

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[str])						->		List[str]:



      """simple docstring"""

      snake_case   :					str							=						{}
      snake_case   :					List[Any]							=						[]

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

                  processed_attn_layers.append(snake_case__)
                  snake_case   :					List[Any]							=						params[base + "out.weight"].shape[-1]
                  snake_case   :					int							=						params[base + "out.weight"].reshape(snake_case__,			snake_case__).T
                  snake_case   :					List[str]							=						params[base + "out.bias"]
            else:
                  snake_case   :					Tuple							=						v
      return new_params


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: Any)						->		str:



      """simple docstring"""

      snake_case   :					Dict							=						CLIP(**snake_case__)
      torch_model.eval()
      snake_case   :					Tuple							=						torch_model.state_dict()

      snake_case   :					List[str]							=						flatten_nested_dict(flax_params["backbone"]["clip"])
      snake_case   :					Union[str, Any]							=						{}

      for flax_key, v in flax_clip_params.items():
            snake_case   :					Union[str, Any]							=						flax_key.replace("/",			".")
            snake_case   :					List[str]							=						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   :					Optional[int]							=						torch_key[5:]

            snake_case   :					Dict							=						torch_key.replace("text_projection.kernel",			"text_projection")
            snake_case   :					Optional[int]							=						torch_key.replace("visual.proj.kernel",			"visual.proj")
            snake_case   :					List[str]							=						torch_key.replace(".scale",			".weight")
            snake_case   :					int							=						torch_key.replace(".kernel",			".weight")

            if "conv" in torch_key or "downsample.0.weight" in torch_key:
                  snake_case   :					Union[str, 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
                  snake_case   :					List[str]							=						v.T

            snake_case   :					Union[str, Any]							=						v

      snake_case   :					List[Any]							=						_convert_attn_layers(snake_case__)
      new_torch_params.update(snake_case__)
      snake_case   :					str							=						{}

      # 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   :					str							=						torch.from_numpy(new_torch_params[name])
                  torch_clip_params[name].copy_(snake_case__)
            else:
                  snake_case   :					Union[str, Any]							=						param

      return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: int,			_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: Optional[Any]=None)						->		Optional[Any]:



      """simple docstring"""

      snake_case   :					Optional[int]							=						Repository(snake_case__,			clone_from=f"""google/{pytorch_dump_folder_path}""")
      repo.git_pull()

      if config_path is not None:
            snake_case   :					Tuple							=						OwlViTConfig.from_pretrained(snake_case__)
      else:
            snake_case   :					str							=						OwlViTConfig()

      snake_case   :					Optional[int]							=						OwlViTModel(snake_case__).eval()
      snake_case   :					Optional[Any]							=						OwlViTForObjectDetection(snake_case__).eval()

      copy_text_model_and_projection(snake_case__,			snake_case__)
      copy_vision_model_and_projection(snake_case__,			snake_case__)
      snake_case   :					Tuple							=						pt_backbone.logit_scale
      copy_flax_attn_params(snake_case__,			snake_case__)

      snake_case   :					Optional[Any]							=						hf_backbone
      copy_class_merge_token(snake_case__,			snake_case__)
      copy_class_box_heads(snake_case__,			snake_case__)

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

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

      # Initialize processor
      snake_case   :					Tuple							=						OwlViTProcessor(image_processor=snake_case__,			tokenizer=snake_case__)
      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_        =   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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_        =   logging.get_logger(__name__)

A_        =   {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

									snake_case   :					Any							=						num_channels
									snake_case   :					Optional[Any]							=						image_size
									snake_case   :					Union[str, Any]							=						patch_size
									snake_case   :					Union[str, Any]							=						hidden_sizes
									snake_case   :					List[Any]							=						neck_hidden_sizes
									snake_case   :					Optional[Any]							=						num_attention_heads
									snake_case   :					Any							=						mlp_ratio
									snake_case   :					List[str]							=						expand_ratio
									snake_case   :					Tuple							=						hidden_act
									snake_case   :					Tuple							=						conv_kernel_size
									snake_case   :					int							=						output_stride
									snake_case   :					Dict							=						hidden_dropout_prob
									snake_case   :					List[str]							=						attention_probs_dropout_prob
									snake_case   :					int							=						classifier_dropout_prob
									snake_case   :					str							=						initializer_range
									snake_case   :					Optional[int]							=						layer_norm_eps
									snake_case   :					Dict							=						qkv_bias

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



			@property
			def      lowerCamelCase   (       self				):
									return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]				)



			@property
			def      lowerCamelCase   (       self				):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]				)
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]				)






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





