



'''simple docstring'''


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase					=  XCLIPTextConfig()

   # derive patch size from model name
   UpperCamelCase					=  model_name.find('patch'    )
   UpperCamelCase					=  int(model_name[start_idx + len('patch'    ) : start_idx + len('patch'    ) + 2]    )
   UpperCamelCase					=  XCLIPVisionConfig(patch_size=A__    ,						num_frames=A__    )

   if "large" in model_name:
      UpperCamelCase					=  768
      UpperCamelCase					=  3_072
      UpperCamelCase					=  12

      UpperCamelCase					=  1_024
      UpperCamelCase					=  4_096
      UpperCamelCase					=  16
      UpperCamelCase					=  24
      UpperCamelCase					=  768
      UpperCamelCase					=  3_072

   if model_name == "xclip-large-patch14-16-frames":
      UpperCamelCase					=  336

   UpperCamelCase					=  XCLIPConfig.from_text_vision_configs(A__    ,						A__    )

   if "large" in model_name:
      UpperCamelCase					=  768

   return config







def 				__lowerCamelCase				(   A__    )       ->	Tuple:






   """simple docstring"""
   # text encoder
   if name == "token_embedding.weight":
      UpperCamelCase					=  name.replace('token_embedding.weight'    ,						'text_model.embeddings.token_embedding.weight'    )
   if name == "positional_embedding":
      UpperCamelCase					=  name.replace('positional_embedding'    ,						'text_model.embeddings.position_embedding.weight'    )
   if "ln_1" in name:
      UpperCamelCase					=  name.replace('ln_1'    ,						'layer_norm1'    )
   if "ln_2" in name:
      UpperCamelCase					=  name.replace('ln_2'    ,						'layer_norm2'    )
   if "c_fc" in name:
      UpperCamelCase					=  name.replace('c_fc'    ,						'fc1'    )
   if "c_proj" in name:
      UpperCamelCase					=  name.replace('c_proj'    ,						'fc2'    )
   if name.startswith('transformer.resblocks'    ):
      UpperCamelCase					=  name.replace('transformer.resblocks'    ,						'text_model.encoder.layers'    )
   if "attn.out_proj" in name and "message" not in name:
      UpperCamelCase					=  name.replace('attn.out_proj'    ,						'self_attn.out_proj'    )
   if "ln_final" in name:
      UpperCamelCase					=  name.replace('ln_final'    ,						'text_model.final_layer_norm'    )
   # visual encoder
   if name == "visual.class_embedding":
      UpperCamelCase					=  name.replace('visual.class_embedding'    ,						'vision_model.embeddings.class_embedding'    )
   if name == "visual.positional_embedding":
      UpperCamelCase					=  name.replace('visual.positional_embedding'    ,						'vision_model.embeddings.position_embedding.weight'    )
   if name.startswith('visual.transformer.resblocks'    ):
      UpperCamelCase					=  name.replace('visual.transformer.resblocks'    ,						'vision_model.encoder.layers'    )
   if "visual.conv1" in name:
      UpperCamelCase					=  name.replace('visual.conv1'    ,						'vision_model.embeddings.patch_embedding'    )
   if "visual.ln_pre" in name:
      UpperCamelCase					=  name.replace('visual.ln_pre'    ,						'vision_model.pre_layernorm'    )
   if "visual.ln_post" in name:
      UpperCamelCase					=  name.replace('visual.ln_post'    ,						'vision_model.post_layernorm'    )
   if "visual.proj" in name:
      UpperCamelCase					=  name.replace('visual.proj'    ,						'visual_projection.weight'    )
   if "text_projection" in name:
      UpperCamelCase					=  name.replace('text_projection'    ,						'text_projection.weight'    )
   # things on top
   if "prompts_visual_proj" in name:
      UpperCamelCase					=  name.replace('prompts_visual_proj'    ,						'prompts_visual_projection'    )
   if "prompts_visual_ln" in name:
      UpperCamelCase					=  name.replace('prompts_visual_ln'    ,						'prompts_visual_layernorm'    )
   # mit
   if name == "mit.positional_embedding":
      UpperCamelCase					=  name.replace('positional'    ,						'position'    )
   if name.startswith('mit.resblocks'    ):
      UpperCamelCase					=  name.replace('mit.resblocks'    ,						'mit.encoder.layers'    )
   # prompts generator
   if name.startswith('prompts_generator.norm'    ):
      UpperCamelCase					=  name.replace('prompts_generator.norm'    ,						'prompts_generator.layernorm'    )

   return name







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      UpperCamelCase					=  orig_state_dict.pop(A__    )

      if "attn.in_proj" in key:
         UpperCamelCase					=  key.split('.'    )
         if key.startswith('visual'    ):
            UpperCamelCase					=  key_split[3]
            UpperCamelCase					=  config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  UpperCamelCase					=  val[
                      :dim, :
                  ]
                  UpperCamelCase					=  val[
                      dim : dim * 2, :
                  ]
                  UpperCamelCase					=  val[
                      -dim:, :
                  ]
               else:
                  UpperCamelCase					=  val[
                      :dim
                  ]
                  UpperCamelCase					=  val[
                      dim : dim * 2
                  ]
                  UpperCamelCase					=  val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  UpperCamelCase					=  val[
                      :dim, :
                  ]
                  UpperCamelCase					=  val[
                      dim : dim * 2, :
                  ]
                  UpperCamelCase					=  val[
                      -dim:, :
                  ]
               else:
                  UpperCamelCase					=  val[:dim]
                  UpperCamelCase					=  val[
                      dim : dim * 2
                  ]
                  UpperCamelCase					=  val[-dim:]
         elif key.startswith('mit'    ):
            UpperCamelCase					=  key_split[2]
            UpperCamelCase					=  config.vision_config.mit_hidden_size
            if "weight" in key:
               UpperCamelCase					=  val[:dim, :]
               UpperCamelCase					=  val[dim : dim * 2, :]
               UpperCamelCase					=  val[-dim:, :]
            else:
               UpperCamelCase					=  val[:dim]
               UpperCamelCase					=  val[dim : dim * 2]
               UpperCamelCase					=  val[-dim:]
         else:
            UpperCamelCase					=  key_split[2]
            UpperCamelCase					=  config.text_config.hidden_size
            if "weight" in key:
               UpperCamelCase					=  val[:dim, :]
               UpperCamelCase					=  val[
                   dim : dim * 2, :
               ]
               UpperCamelCase					=  val[-dim:, :]
            else:
               UpperCamelCase					=  val[:dim]
               UpperCamelCase					=  val[
                   dim : dim * 2
               ]
               UpperCamelCase					=  val[-dim:]
      else:
         UpperCamelCase					=  rename_key(A__    )
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            UpperCamelCase					=  val.T
         UpperCamelCase					=  val

   return orig_state_dict







def 				__lowerCamelCase				(   A__    )       ->	Optional[Any]:






   """simple docstring"""
   if num_frames == 8:
      UpperCamelCase					=  'eating_spaghetti_8_frames.npy'
   elif num_frames == 16:
      UpperCamelCase					=  'eating_spaghetti.npy'
   elif num_frames == 32:
      UpperCamelCase					=  'eating_spaghetti_32_frames.npy'
   UpperCamelCase					=  hf_hub_download(
       repo_id='hf-internal-testing/spaghetti-video'    ,						filename=A__    ,						repo_type='dataset'    ,						)
   UpperCamelCase					=  np.load(A__    )
   return list(A__    )







def 				__lowerCamelCase				(   A__    ,						A__=None    ,						A__=False    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  {
       # fully supervised kinetics-400 checkpoints
       'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
       'xclip-base-patch32-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
       ),
       'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
       'xclip-base-patch16-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
       ),
       'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
       'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
       # fully supervised kinetics-600 checkpoints
       'xclip-base-patch16-kinetics-600': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
       ),
       'xclip-base-patch16-kinetics-600-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
       ),
       'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
       # few shot
       'xclip-base-patch16-hmdb-2-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
       ),
       'xclip-base-patch16-hmdb-4-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
       ),
       'xclip-base-patch16-hmdb-8-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
       ),
       'xclip-base-patch16-hmdb-16-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
       ),
       'xclip-base-patch16-ucf-2-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
       ),
       'xclip-base-patch16-ucf-4-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
       ),
       'xclip-base-patch16-ucf-8-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
       ),
       'xclip-base-patch16-ucf-16-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
       ),
       # zero shot
       'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
   }

   UpperCamelCase					=  model_to_url[model_name]
   UpperCamelCase					=  8
   if "16-frames" in model_name:
      UpperCamelCase					=  16
   elif "shot" in model_name:
      UpperCamelCase					=  32

   UpperCamelCase					=  get_xclip_config(A__    ,						A__    )
   UpperCamelCase					=  XCLIPModel(A__    )
   model.eval()

   if "drive" in checkpoint_url:
      UpperCamelCase					=  'pytorch_model.bin'
      gdown.cached_download(A__    ,						A__    ,						quiet=A__    )
      UpperCamelCase					=  torch.load(A__    ,						map_location='cpu'    )['model']
   else:
      UpperCamelCase					=  torch.hub.load_state_dict_from_url(A__    )['model']

   UpperCamelCase					=  convert_state_dict(A__    ,						A__    )

   UpperCamelCase					=  XCLIPModel(A__    )
   UpperCamelCase				,      UpperCamelCase					=  model.load_state_dict(A__    ,						strict=A__    )
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   UpperCamelCase					=  336 if model_name == 'xclip-large-patch14-16-frames' else 224
   UpperCamelCase					=  VideoMAEImageProcessor(size=A__    )
   UpperCamelCase					=  CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'    )
   UpperCamelCase					=  CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'    )
   UpperCamelCase					=  XCLIPProcessor(image_processor=A__    ,						tokenizer=A__    )

   UpperCamelCase					=  prepare_video(A__    )
   UpperCamelCase					=  processor(
       text=['playing sports', 'eating spaghetti', 'go shopping']    ,						videos=A__    ,						return_tensors='pt'    ,						padding=A__    )

   print('Shape of pixel values:'    ,						inputs.pixel_values.shape    )

   with torch.no_grad():
      UpperCamelCase					=  model(**A__    )

   # Verify outputs
   UpperCamelCase					=  outputs.logits_per_video
   UpperCamelCase					=  logits_per_video.softmax(dim=1    )
   print('Probs:'    ,						A__    )
   # kinetics-400
   if model_name == "xclip-base-patch32":
      UpperCamelCase					=  torch.tensor([[0.0_019, 0.9_951, 0.0_030]]    )
   elif model_name == "xclip-base-patch32-16-frames":
      UpperCamelCase					=  torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]    )
   elif model_name == "xclip-base-patch16":
      UpperCamelCase					=  torch.tensor([[0.0_083, 0.9_681, 0.0_236]]    )
   elif model_name == "xclip-base-patch16-16-frames":
      UpperCamelCase					=  torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]    )
   elif model_name == "xclip-large-patch14":
      UpperCamelCase					=  torch.tensor([[0.0_062, 0.9_864, 0.0_075]]    )
   elif model_name == "xclip-large-patch14-16-frames":
      UpperCamelCase					=  torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]    )
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      UpperCamelCase					=  torch.tensor([[0.0_555, 0.8_914, 0.0_531]]    )
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      UpperCamelCase					=  torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]    )
   elif model_name == "xclip-large-patch14-kinetics-600":
      UpperCamelCase					=  torch.tensor([[0.0_036, 0.9_920, 0.0_045]]    )
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      UpperCamelCase					=  torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]    )
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      UpperCamelCase					=  torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]    )
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      UpperCamelCase					=  torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]    )
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      UpperCamelCase					=  torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]    )
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      UpperCamelCase					=  torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]    )
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      UpperCamelCase					=  torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]    )
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      UpperCamelCase					=  torch.tensor([[0.0_027, 0.9_904, 0.0_070]]    )
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      UpperCamelCase					=  torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]    )
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      UpperCamelCase					=  torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]    )
   else:
      raise ValueError(F"""Model name {model_name} not supported"""    )
   assert torch.allclose(A__    ,						A__    ,						atol=1e-3    )
   print('Looks ok!'    )

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
      model.save_pretrained(A__    )

   if push_to_hub:
      print('Pushing model, processor and slow tokenizer files to the hub...'    )
      model.push_to_hub(A__    ,						organization='nielsr'    )
      processor.push_to_hub(A__    ,						organization='nielsr'    )
      slow_tokenizer.push_to_hub(A__    ,						organization='nielsr'    )


if __name__ == "__main__":
   _lowerCamelCase	:       Union[str, Any]      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="xclip-base-patch32",
       type=str,
       help="Name of the model.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   _lowerCamelCase	:       str      	=	parser.parse_args()
   convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''


from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__=1e-1_2    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  jnp.divide(emb_a.T    ,						jnp.clip(jnp.linalg.norm(A__    ,						axis=1    )    ,						a_min=A__    )    ).T
   UpperCamelCase					=  jnp.divide(emb_a.T    ,						jnp.clip(jnp.linalg.norm(A__    ,						axis=1    )    ,						a_min=A__    )    ).T
   return jnp.matmul(A__    ,						norm_emb_a.T    )

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  jnp.floataa






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  FlaxCLIPVisionModule(self.config.vision_config )
       UpperCamelCase					=  nn.Dense(self.config.projection_dim  ,						use_bias=UpperCamelCase__  ,						dtype=self.dtype )

       UpperCamelCase					=  self.param('concept_embeds'  ,						jax.nn.initializers.ones  ,						(1_7, self.config.projection_dim) )
       UpperCamelCase					=  self.param(
           'special_care_embeds'  ,						jax.nn.initializers.ones  ,						(3, self.config.projection_dim) )

       UpperCamelCase					=  self.param('concept_embeds_weights'  ,						jax.nn.initializers.ones  ,						(1_7,) )
       UpperCamelCase					=  self.param('special_care_embeds_weights'  ,						jax.nn.initializers.ones  ,						(3,) )






    def __call__(  self      :					str  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  self.vision_model(UpperCamelCase__ )[1]
       UpperCamelCase					=  self.visual_projection(UpperCamelCase__ )

       UpperCamelCase					=  jax_cosine_distance(UpperCamelCase__  ,						self.special_care_embeds )
       UpperCamelCase					=  jax_cosine_distance(UpperCamelCase__  ,						self.concept_embeds )

       # increase this value to create a stronger `nfsw` filter
       # at the cost of increasing the possibility of filtering benign image inputs
       UpperCamelCase					=  0.0

       UpperCamelCase					=  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
       UpperCamelCase					=  jnp.round(UpperCamelCase__  ,						3 )
       UpperCamelCase					=  jnp.any(special_scores > 0  ,						axis=1  ,						keepdims=UpperCamelCase__ )
       # Use a lower threshold if an image has any special care concept
       UpperCamelCase					=  is_special_care * 0.0_1

       UpperCamelCase					=  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
       UpperCamelCase					=  jnp.round(UpperCamelCase__  ,						3 )
       UpperCamelCase					=  jnp.any(concept_scores > 0  ,						axis=1 )

       return has_nsfw_concepts

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  CLIPConfig
    _SCREAMING_SNAKE_CASE	  =  """clip_input"""
    _SCREAMING_SNAKE_CASE	  =  FlaxStableDiffusionSafetyCheckerModule






    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					CLIPConfig  ,						UpperCamelCase__      :					Optional[Tuple] = None  ,						UpperCamelCase__      :					int = 0  ,						UpperCamelCase__      :					jnp.dtype = jnp.floataa  ,						UpperCamelCase__      :					bool = True  ,						**UpperCamelCase__      :					List[str]  ,						):


       """simple docstring"""







       if input_shape is None:
          UpperCamelCase					=  (1, 2_2_4, 2_2_4, 3)
       UpperCamelCase					=  self.module_class(config=UpperCamelCase__  ,						dtype=UpperCamelCase__  ,						**UpperCamelCase__ )
       super().__init__(UpperCamelCase__  ,						UpperCamelCase__  ,						input_shape=UpperCamelCase__  ,						seed=UpperCamelCase__  ,						dtype=UpperCamelCase__  ,						_do_init=_do_init )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					jax.random.KeyArray  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					FrozenDict = None ):


       """simple docstring"""







       UpperCamelCase					=  jax.random.normal(UpperCamelCase__  ,						UpperCamelCase__ )

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

       UpperCamelCase					=  self.module.init(UpperCamelCase__  ,						UpperCamelCase__ )['params']

       return random_params






    def __call__(  self      :					List[Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					dict = None  ,						):


       """simple docstring"""







       UpperCamelCase					=  jnp.transpose(UpperCamelCase__  ,						(0, 2, 3, 1) )

       return self.module.apply(
           {'params': params or self.params}  ,						jnp.array(UpperCamelCase__  ,						dtype=jnp.floataa )  ,						rngs={}  ,						)

