

from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


__A    	=			logging.get_logger(__name__)



class 						lowercase_					(						nn.Module  ):
      UpperCamelCase_    :	HybridCLIPConfig
      UpperCamelCase_    :	jnp.dtype		   =       jnp.floataa



      def 		UpperCamelCase_	(    self   :       Any	)					->  Union[str, Any]:
        _snake_case		=							self.config.text_config
        _snake_case		=							self.config.vision_config

        _snake_case		=							self.config.projection_dim
        _snake_case		=							text_config.hidden_size
        _snake_case		=							vision_config.hidden_size

        _snake_case		=							FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
        _snake_case		=							FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,      A__	).module_class

        _snake_case		=							text_module(A__    ,      dtype=self.dtype	)
        _snake_case		=							vision_module(A__    ,      dtype=self.dtype	)

        _snake_case		=							nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02	)    ,      use_bias=A__    ,      )
        _snake_case		=							nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02	)    ,      use_bias=A__    ,      )
        _snake_case		=							self.param('''logit_scale'''    ,      jax.nn.initializers.ones    ,      []	)







      def __call__(    self   :       Union[str, Any]    ,      A__   :       Optional[int]=None    ,      A__   :       List[str]=None    ,      A__   :       int=None    ,      A__   :       Optional[int]=None    ,      A__   :       Tuple=None    ,      A__   :       bool = True    ,      A__   :       Dict=None    ,      A__   :       Dict=None    ,      A__   :       List[Any]=None    ,      )					->  int:
        _snake_case		=							return_dict if return_dict is not None else self.config.return_dict

        _snake_case		=							self.vision_model(
            pixel_values=A__    ,      deterministic=A__    ,      output_attentions=A__    ,      output_hidden_states=A__    ,      return_dict=A__    ,      )

        _snake_case		=							self.text_model(
            input_ids=A__    ,      attention_mask=A__    ,      token_type_ids=A__    ,      position_ids=A__    ,      deterministic=A__    ,      output_attentions=A__    ,      output_hidden_states=A__    ,      return_dict=A__    ,      )

        _snake_case		=							vision_outputs[1]
        _snake_case		=							self.visual_projection(A__	)

        _snake_case		=							text_outputs[1]
        _snake_case		=							self.text_projection(A__	)

        # normalized features
        _snake_case		=							image_embeds / jnp.linalg.norm(A__    ,      axis=-1    ,      keepdims=A__	)
        _snake_case		=							text_embeds / jnp.linalg.norm(A__    ,      axis=-1    ,      keepdims=A__	)

        # cosine similarity as logits
        _snake_case		=							jnp.exp(self.logit_scale	)
        _snake_case		=							jnp.matmul(A__    ,      image_embeds.T	) * logit_scale
        _snake_case		=							logits_per_text.T

        if not return_dict:
          return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

        return FlaxCLIPOutput(
            logits_per_image=A__    ,      logits_per_text=A__    ,      text_embeds=A__    ,      image_embeds=A__    ,      text_model_output=A__    ,      vision_model_output=A__    ,      )



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	List[str]		   =       HybridCLIPConfig
      UpperCamelCase_    :	Optional[Any]		   =       FlaxHybridCLIPModule



      def __init__(    self   :       Dict    ,      A__   :       HybridCLIPConfig    ,      A__   :       Optional[Tuple] = None    ,      A__   :       int = 0    ,      A__   :       jnp.dtype = jnp.floataa    ,      **A__   :       int    ,      )					->  int:
        if input_shape is None:
          _snake_case		=							((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

        _snake_case		=							self.module_class(config=A__    ,      dtype=A__    ,      **A__	)
        super().__init__(A__    ,      A__    ,      input_shape=A__    ,      seed=A__    ,      dtype=A__	)



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       jax.random.PRNGKey    ,      A__   :       Tuple    ,      A__   :       FrozenDict = None	)					->  FrozenDict:
        # init input tensor
        _snake_case		=							jnp.zeros(input_shape[0]    ,      dtype='''i4'''	)
        _snake_case		=							jnp.broadcast_to(jnp.arange(jnp.atleast_ad(A__	).shape[-1]	)    ,      input_shape[0]	)
        _snake_case		=							jnp.ones_like(A__	)
        _snake_case		=							jnp.ones_like(A__	)

        _snake_case		=							jax.random.normal(A__    ,      input_shape[1]	)

        _snake_case,  _snake_case		=							jax.random.split(A__	)
        _snake_case		=							{'''params''': params_rng, '''dropout''': dropout_rng}

        return self.module.init(A__    ,      A__    ,      A__    ,      A__    ,      A__    ,      A__	)["params"]



      def __call__(    self   :       int    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]    ,      A__   :       List[str]=None    ,      A__   :       Union[str, Any]=None    ,      A__   :       Optional[Any]=None    ,      A__   :       dict = None    ,      A__   :       jax.random.PRNGKey = None    ,      A__   :       bool = False    ,      A__   :       Optional[bool] = None    ,      A__   :       Optional[bool] = None    ,      A__   :       Optional[bool] = None    ,      )					->  Optional[Any]:
        _snake_case		=							output_attentions if output_attentions is not None else self.config.output_attentions
        _snake_case		=							(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _snake_case		=							return_dict if return_dict is not None else self.config.return_dict

        if position_ids is None:
          _snake_case		=							jnp.broadcast_to(jnp.arange(jnp.atleast_ad(A__	).shape[-1]	)    ,      input_ids.shape	)

        if token_type_ids is None:
          _snake_case		=							jnp.zeros_like(A__	)

        if attention_mask is None:
          _snake_case		=							jnp.ones_like(A__	)

        # Handle any PRNG if needed
        _snake_case		=							{}
        if dropout_rng is not None:
          _snake_case		=							dropout_rng

        return self.module.apply(
            {'''params''': params or self.params}    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype=jnp.floataa	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      not train    ,      A__    ,      A__    ,      A__    ,      rngs=A__    ,      )



      def 		UpperCamelCase_	(    self   :       str    ,      A__   :       str    ,      A__   :       Any=None    ,      A__   :       Tuple=None    ,      A__   :       Union[str, Any]=None    ,      A__   :       dict = None    ,      A__   :       jax.random.PRNGKey = None    ,      A__   :       List[str]=False    ,      )					->  Tuple:
        if position_ids is None:
          _snake_case		=							jnp.broadcast_to(jnp.arange(jnp.atleast_ad(A__	).shape[-1]	)    ,      input_ids.shape	)

        if token_type_ids is None:
          _snake_case		=							jnp.zeros_like(A__	)

        if attention_mask is None:
          _snake_case		=							jnp.ones_like(A__	)

        # Handle any PRNG if needed
        _snake_case		=							{}
        if dropout_rng is not None:
          _snake_case		=							dropout_rng

        def _get_features(A__   :       Optional[int]    ,      A__   :       str    ,      A__   :       int    ,      A__   :       Optional[Any]    ,      A__   :       Tuple    ,      A__   :       int	):
          _snake_case		=							module.text_model(
              input_ids=A__    ,      attention_mask=A__    ,      position_ids=A__    ,      token_type_ids=A__    ,      deterministic=A__    ,      )
          _snake_case		=							text_outputs[1]
          _snake_case		=							module.text_projection(A__	)
          return text_features

        return self.module.apply(
            {'''params''': params or self.params}    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      jnp.array(A__    ,      dtype='''i4'''	)    ,      not train    ,      method=_get_features    ,      rngs=A__    ,      )



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       List[str]    ,      A__   :       dict = None    ,      A__   :       jax.random.PRNGKey = None    ,      A__   :       Optional[Any]=False	)					->  List[str]:
        _snake_case		=							{}
        if dropout_rng is not None:
          _snake_case		=							dropout_rng

        def _get_features(A__   :       Dict    ,      A__   :       Dict    ,      A__   :       Tuple	):
          _snake_case		=							module.vision_model(pixel_values=A__    ,      deterministic=A__	)
          _snake_case		=							vision_outputs[1]  # pooled_output
          _snake_case		=							module.visual_projection(A__	)
          return image_features

        return self.module.apply(
            {'''params''': params or self.params}    ,      jnp.array(A__    ,      dtype=jnp.floataa	)    ,      not train    ,      method=_get_features    ,      rngs=A__    ,      )







      @classmethod
      def 		UpperCamelCase_	(    cls   :       Any    ,      A__   :       str = None    ,      A__   :       str = None    ,      *A__   :       Tuple    ,      **A__   :       str    ,      )					->  FlaxPreTrainedModel:
        _snake_case		=							{
            argument[len('''text_'''	) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''	)
        }

        _snake_case		=							{
            argument[len('''vision_'''	) :]: value for argument, value in kwargs.items() if argument.startswith('''vision_'''	)
        }

        # remove text, vision kwargs from kwargs
        for key in kwargs_text.keys():
          del kwargs["text_" + key]
        for key in kwargs_vision.keys():
          del kwargs["vision_" + key]

        # Load and initialize the text and vision model
        _snake_case		=							kwargs_text.pop('''model'''    ,      A__	)
        if text_model is None:
          assert (
              text_model_name_or_path is not None
          ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
          from transformers import FlaxAutoModel

          if "config" not in kwargs_text:
            from transformers import AutoConfig

            _snake_case		=							AutoConfig.from_pretrained(A__	)
            _snake_case		=							text_config

          _snake_case		=							FlaxAutoModel.from_pretrained(A__    ,      *A__    ,      **A__	)

        _snake_case		=							kwargs_vision.pop('''model'''    ,      A__	)
        if vision_model is None:
          assert (
              vision_model_name_or_path is not None
          ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
          from transformers import FlaxAutoModel

          if "config" not in kwargs_vision:
            from transformers import AutoConfig

            _snake_case		=							AutoConfig.from_pretrained(A__	)
            _snake_case		=							vision_config

          _snake_case		=							FlaxAutoModel.from_pretrained(A__    ,      *A__    ,      **A__	)

        # instantiate config with corresponding kwargs
        _snake_case		=							kwargs.pop('''dtype'''    ,      jnp.floataa	)
        _snake_case		=							HybridCLIPConfig.from_text_vision_configs(text_model.config    ,      vision_model.config    ,      **A__	)

        # init model
        _snake_case		=							cls(A__    ,      *A__    ,      dtype=A__    ,      **A__	)

        if vision_config.model_type == "clip":
          _snake_case		=							vision_model.params['''vision_model''']
          _snake_case		=							vision_model.params['''visual_projection''']['''kernel''']
        else:
          _snake_case		=							vision_model.params

        _snake_case		=							text_model.params

        return model




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=			logging.get_logger(__name__)



def    snake_case_(_UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  if "backbone.0.body" in orig_key:
    _snake_case		=							orig_key.replace('''backbone.0.body'''				, '''backbone.conv_encoder.model'''  )
  if "transformer" in orig_key:
    _snake_case		=							orig_key.replace('''transformer.'''				, ''''''  )
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm1'''				, '''self_attn_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm1'''				, '''encoder_attn_layer_norm'''  )
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm2'''				, '''final_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm2'''				, '''self_attn_layer_norm'''  )
  if "norm3" in orig_key:
    _snake_case		=							orig_key.replace('''norm3'''				, '''final_layer_norm'''  )
  if "linear1" in orig_key:
    _snake_case		=							orig_key.replace('''linear1'''				, '''fc1'''  )
  if "linear2" in orig_key:
    _snake_case		=							orig_key.replace('''linear2'''				, '''fc2'''  )
  if "query_embed" in orig_key:
    _snake_case		=							orig_key.replace('''query_embed'''				, '''query_position_embeddings'''  )
  if "cross_attn" in orig_key:
    _snake_case		=							orig_key.replace('''cross_attn'''				, '''encoder_attn'''  )

  return orig_key



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""  )
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""  )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case		=							in_proj_weight[:256, :]
    _snake_case		=							in_proj_bias[:256]
    _snake_case		=							in_proj_weight[256:512, :]
    _snake_case		=							in_proj_bias[256:512]
    _snake_case		=							in_proj_weight[-256:, :]
    _snake_case		=							in_proj_bias[-256:]



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case		=							Image.open(requests.get(_UpperCamelCase				, stream=_UpperCamelCase  ).raw  )

  return im



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, )							->			Dict:



  """simple docstring"""






  _snake_case		=							DeformableDetrConfig()
  # set config attributes
  if single_scale:
    _snake_case		=							1
  _snake_case		=							dilation
  _snake_case		=							with_box_refine
  _snake_case		=							two_stage
  # set labels
  _snake_case		=							91
  _snake_case		=							'''huggingface/label-files'''
  _snake_case		=							'''coco-detection-id2label.json'''
  _snake_case		=							json.load(open(cached_download(hf_hub_url(_UpperCamelCase				, _UpperCamelCase				, repo_type='''dataset'''  )  )				, '''r'''  )  )
  _snake_case		=							{int(_UpperCamelCase  ): v for k, v in idalabel.items()}
  _snake_case		=							idalabel
  _snake_case		=							{v: k for k, v in idalabel.items()}

  # load image processor
  _snake_case		=							DeformableDetrImageProcessor(format='''coco_detection'''  )

  # prepare image
  _snake_case		=							prepare_img()
  _snake_case		=							image_processor(images=_UpperCamelCase				, return_tensors='''pt'''  )
  _snake_case		=							encoding['''pixel_values''']

  logger.info('''Converting model...'''  )

  # load original state dict
  _snake_case		=							torch.load(_UpperCamelCase				, map_location='''cpu'''  )['''model''']
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case		=							state_dict.pop(_UpperCamelCase  )
    _snake_case		=							val
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCamelCase  )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _snake_case		=							'''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
      _snake_case		=							state_dict.pop(_UpperCamelCase  )
      _snake_case		=							val
    # finally, create HuggingFace model and load state dict
  _snake_case		=							DeformableDetrForObjectDetection(_UpperCamelCase  )
  model.load_state_dict(_UpperCamelCase  )
  model.eval()

  _snake_case		=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _snake_case		=							model(pixel_values.to(_UpperCamelCase  )  )

  _snake_case		=							torch.tensor(
      [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
  _snake_case		=							torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]  )

  if single_scale:
    _snake_case		=							torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
    _snake_case		=							torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]  )

  if single_scale and dilation:
    _snake_case		=							torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
    _snake_case		=							torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]  )

  if with_box_refine:
    _snake_case		=							torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
    _snake_case		=							torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]  )

  if with_box_refine and two_stage:
    _snake_case		=							torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
    _snake_case		=							torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]  )

  print('''Logits:'''				, outputs.logits[0, :3, :3]  )

  assert torch.allclose(outputs.logits[0, :3, :3]				, expected_logits.to(_UpperCamelCase  )				, atol=1E-4  )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]				, expected_boxes.to(_UpperCamelCase  )				, atol=1E-4  )

  print('''Everything ok!'''  )

  # Save model and image processor
  logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""  )
  Path(_UpperCamelCase  ).mkdir(exist_ok=_UpperCamelCase  )
  model.save_pretrained(_UpperCamelCase  )
  image_processor.save_pretrained(_UpperCamelCase  )

  # Push to hub
  if push_to_hub:
    _snake_case		=							'''deformable-detr'''
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print('''Pushing model to hub...'''  )
    model.push_to_hub(repo_path_or_name=_UpperCamelCase				, organization='''nielsr'''				, commit_message='''Add model'''  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     type=str,
     default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
     help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
 )
 parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
 parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
 parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
 parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the folder to output PyTorch model.''',
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 __A    	=			parser.parse_args()
 convert_deformable_detr_checkpoint(
     args.checkpoint_path,
     args.single_scale,
     args.dilation,
     args.with_box_refine,
     args.two_stage,
     args.pytorch_dump_folder_path,
     args.push_to_hub,
 )

