





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= TaConfig.from_pretrained(_UpperCAmelCase		)
 snake_case__										= FlaxTaForConditionalGeneration(config=_UpperCAmelCase		)
 snake_case__										= checkpoints.load_tax_checkpoint(_UpperCAmelCase		)

 snake_case__										= "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

 # Encoder
 for layer_index in range(config.num_layers		):
  snake_case__										= f"layers_{str(_UpperCAmelCase		)}"

  # Self-Attention
  snake_case__										= tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
  snake_case__										= tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
  snake_case__										= tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
  snake_case__										= tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

  # Layer Normalization
  snake_case__										= tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

  if split_mlp_wi:
   snake_case__										= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
   snake_case__										= tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
  else:
   snake_case__										= tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

  snake_case__										= tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

  # Layer Normalization
  snake_case__										= tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

  # Assigning
  snake_case__										= tax_attention_key
  snake_case__										= tax_attention_out
  snake_case__										= tax_attention_query
  snake_case__										= tax_attention_value

  snake_case__										= tax_attention_layer_norm

  if split_mlp_wi:
   snake_case__										= tax_mlp_wi_a
   snake_case__										= tax_mlp_wi_a
  else:
   snake_case__										= tax_mlp_wi

  snake_case__										= tax_mlp_wo
  snake_case__										= tax_mlp_layer_norm

 # Only for layer 0:
 snake_case__										= tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
 snake_case__										= tax_encoder_rel_embedding

 # Assigning
 snake_case__										= tax_model["target"]["encoder"]["encoder_norm"]["scale"]
 snake_case__										= tax_encoder_norm

 # Decoder
 for layer_index in range(config.num_decoder_layers		):
  snake_case__										= f"layers_{str(_UpperCAmelCase		)}"

  # Self-Attention
  snake_case__										= tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

  # Layer Normalization
  snake_case__										= tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
      "scale"
  ]

  # Encoder-Decoder-Attention
  snake_case__										= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
      "kernel"
  ]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
      "kernel"
  ]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
      "kernel"
  ]
  snake_case__										= tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
      "kernel"
  ]

  # Layer Normalization
  snake_case__										= tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

  # MLP
  if split_mlp_wi:
   snake_case__										= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
   snake_case__										= tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
  else:
   snake_case__										= tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

  snake_case__										= tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

  # Layer Normalization
  snake_case__										= tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

  # Assigning
  snake_case__										= tax_attention_key
  snake_case__										= tax_attention_out
  snake_case__										= tax_attention_query
  snake_case__										= tax_attention_value

  snake_case__										= tax_pre_attention_layer_norm

  snake_case__										= tax_enc_dec_attention_key
  snake_case__										= tax_enc_dec_attention_out
  snake_case__										= tax_enc_dec_attention_query
  snake_case__										= tax_enc_dec_attention_value

  snake_case__										= tax_cross_layer_norm

  if split_mlp_wi:
   snake_case__										= tax_mlp_wi_a
   snake_case__										= tax_mlp_wi_a
  else:
   snake_case__										= tax_mlp_wi

  snake_case__										= tax_mlp_wo

  snake_case__										= txa_mlp_layer_norm

 # Decoder Normalization
 snake_case__										= tax_model["target"]["decoder"]["decoder_norm"]["scale"]
 snake_case__										= txa_decoder_norm

 # Only for layer 0:
 snake_case__										= tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
 snake_case__										= tax_decoder_rel_embedding

 # Token Embeddings
 snake_case__										= tax_model["target"]["token_embedder"]["embedding"]
 snake_case__										= txa_token_embeddings

 # LM Head (only in v1.1 checkpoints)
 if "logits_dense" in tax_model["target"]["decoder"]:
  snake_case__										= tax_model["target"]["decoder"]["logits_dense"]["kernel"]

 flax_model.save_pretrained(_UpperCAmelCase		)
 print("T5X Model was sucessfully converted!"		)


if __name__ == "__main__":
   a__    :   Union[str, Any]    						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
   )
   parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
   parser.add_argument(
       """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
   )
   a__    :   List[str]    						= parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








"""simple docstring"""


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a__    :   Any    						= logging.get_logger(__name__)



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							List[nn.Module] 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							list 							=   field(default_factory=__lowerCAmelCase							)

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= len(list(m.modules()   )   ) == 1 or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.Convad   ) or isinstance(__SCREAMING_SNAKE_CASE      ,    nn.BatchNormad   )
        if has_not_submodules:
         if not isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          self.traced.append(__SCREAMING_SNAKE_CASE   )

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook   )   )
        self.module(__SCREAMING_SNAKE_CASE   )
        [x.remove() for x in self.handles]
        return self

       @property
       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        return list(filter(lambda __SCREAMING_SNAKE_CASE   : len(list(x.state_dict().keys()   )   ) > 0      ,    self.traced   )   )



@dataclass
class    lowercase_ :
       snake_case__ :							nn.Module
       snake_case__ :							nn.Module
       snake_case__ :							int 							=   0
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)
       snake_case__ :							List 							=   field(default_factory=__lowerCAmelCase							)

       def __call__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= Tracker(self.dest   )(__SCREAMING_SNAKE_CASE   ).parametrized
        snake_case__										= Tracker(self.src   )(__SCREAMING_SNAKE_CASE   ).parametrized

        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.src_skip      ,    __SCREAMING_SNAKE_CASE   )   )
        snake_case__										= list(filter(lambda __SCREAMING_SNAKE_CASE   : type(__SCREAMING_SNAKE_CASE   ) not in self.dest_skip      ,    __SCREAMING_SNAKE_CASE   )   )

        if len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
         raise Exception(
             F"Numbers of operations are different. Source module has {len(__SCREAMING_SNAKE_CASE   )} operations while"
             F" destination module has {len(__SCREAMING_SNAKE_CASE   )}."   )

        for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
         dest_m.load_state_dict(src_m.state_dict()   )
         if self.verbose == 1:
          print(F"Transfered from={src_m} to={dest_m}"   )





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     nn.Module:

 '''simple docstring'''






 snake_case__										= from_model.state_dict()
 snake_case__										= our_model.state_dict()
 snake_case__										= our_model.config
 snake_case__										= []
 for stage_idx in range(len(config.hidden_sizes		)		):
  for block_id in range(config.depths[stage_idx]		):
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_1"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

   all_keys.append((from_key, to_key)		)
   snake_case__										= f"block{stage_idx + 1}.{block_id}.layer_scale_2"
   snake_case__										= f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

   all_keys.append((from_key, to_key)		)

 for from_key, to_key in all_keys:
  snake_case__										= from_state_dict.pop(_UpperCAmelCase		)

 our_model.load_state_dict(_UpperCAmelCase		)
 return our_model





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase = True		,   )			->     Union[str, Any]:

 '''simple docstring'''






 print(f"Downloading weights for {name}..."		)
 snake_case__										= cached_download(_UpperCAmelCase		)
 print(f"Converting {name}..."		)
 snake_case__										= torch.load(_UpperCAmelCase		)["state_dict"]
 from_model.load_state_dict(_UpperCAmelCase		)
 from_model.eval()
 with torch.no_grad():
  snake_case__										= VanForImageClassification(_UpperCAmelCase		).eval()
  snake_case__										= ModuleTransfer(src=_UpperCAmelCase		,   dest=_UpperCAmelCase		)
  snake_case__										= torch.randn((1, 3, 2_2_4, 2_2_4)		)
  module_transfer(_UpperCAmelCase		)
  snake_case__										= copy_parameters(_UpperCAmelCase		,   _UpperCAmelCase		)

 if not torch.allclose(from_model(_UpperCAmelCase		)		,   our_model(_UpperCAmelCase		).logits		):
  raise ValueError("The model logits don't match the original one."		)

 snake_case__										= name
 print(_UpperCAmelCase		)

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add model"		,   use_temp_dir=_UpperCAmelCase		,   )

  # we can use the convnext one
  snake_case__										= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,   commit_message="Add image processor"		,   use_temp_dir=_UpperCAmelCase		,   )

  print(f"Pushed {checkpoint_name}"		)





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase = None		,   _UpperCAmelCase = True		)			->     Union[str, Any]:

 '''simple docstring'''






 snake_case__										= "imagenet-1k-id2label.json"
 snake_case__										= 1_0_0_0

 snake_case__										= "huggingface/label-files"
 snake_case__										= num_labels
 snake_case__										= json.load(open(hf_hub_download(_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()}

 snake_case__										= partial(_UpperCAmelCase		,   num_labels=_UpperCAmelCase		,   idalabel=_UpperCAmelCase		,   labelaid=_UpperCAmelCase		)

 snake_case__										= {
     "van-tiny": ImageNetPreTrainedConfig(
         hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]		,   depths=[3, 3, 5, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-small": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[2, 2, 4, 2]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-base": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 3, 1_2, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
     "van-large": ImageNetPreTrainedConfig(
         hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		,   depths=[3, 5, 2_7, 3]		,   mlp_ratios=[8, 8, 4, 4]		,   ),
 }

 snake_case__										= {
     "van-tiny": van_tiny,
     "van-small": van_small,
     "van-base": van_base,
     "van-large": van_large,
 }

 snake_case__										= {
     "van-tiny": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
     ),
     "van-small": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
     ),
     "van-base": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
     ),
     "van-large": (
         "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
     ),
 }

 if model_name:
  convert_weight_and_push(
      _UpperCAmelCase		,   names_to_config[model_name]		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(
       _UpperCAmelCase		,   _UpperCAmelCase		,   checkpoint=names_to_original_checkpoints[model_name]		,   from_model=names_to_original_models[model_name]()		,   save_directory=_UpperCAmelCase		,   push_to_hub=_UpperCAmelCase		,   )


if __name__ == "__main__":
   a__    :   Any    						= argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model-name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--van_dir""",
       required=True,
       type=Path,
       help=(
           """A path to VAN's original implementation directory. You can download from here:"""
           """ https://github.com/Visual-Attention-Network/VAN-Classification"""
       ),
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   a__    :   Any    						= parser.parse_args()
   a__    :   Path    						= args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   a__    :   int    						= args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

