"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]     )	->				Any:
  # vision encoder
  if "img_encoder.pos_embed" in name:
    _lowerCAmelCase						:	Optional[Any]        =					name.replace("""img_encoder.pos_embed"""      ,"""vision_model.embeddings.position_embeddings"""     )
  if "img_encoder.patch_embed.proj" in name:
    _lowerCAmelCase						:	List[str]        =					name.replace("""img_encoder.patch_embed.proj"""      ,"""vision_model.embeddings.patch_embeddings.projection"""     )
  if "img_encoder.patch_embed.norm" in name:
    _lowerCAmelCase						:	str        =					name.replace("""img_encoder.patch_embed.norm"""      ,"""vision_model.embeddings.layernorm"""     )
  if "img_encoder.layers" in name:
    _lowerCAmelCase						:	Union[str, Any]        =					name.replace("""img_encoder.layers"""      ,"""vision_model.encoder.stages"""     )
  if "blocks" in name and "res" not in name:
    _lowerCAmelCase						:	Optional[int]        =					name.replace("""blocks"""      ,"""layers"""     )
  if "attn" in name and "pre_assign" not in name:
    _lowerCAmelCase						:	Union[str, Any]        =					name.replace("""attn"""      ,"""self_attn"""     )
  if "proj" in name and "self_attn" in name and "text" not in name:
    _lowerCAmelCase						:	List[str]        =					name.replace("""proj"""      ,"""out_proj"""     )
  if "pre_assign_attn.attn.proj" in name:
    _lowerCAmelCase						:	List[Any]        =					name.replace("""pre_assign_attn.attn.proj"""      ,"""pre_assign_attn.attn.out_proj"""     )
  if "norm1" in name:
    _lowerCAmelCase						:	Optional[int]        =					name.replace("""norm1"""      ,"""layer_norm1"""     )
  if "norm2" in name and "pre_assign" not in name:
    _lowerCAmelCase						:	Union[str, Any]        =					name.replace("""norm2"""      ,"""layer_norm2"""     )
  if "img_encoder.norm" in name:
    _lowerCAmelCase						:	int        =					name.replace("""img_encoder.norm"""      ,"""vision_model.layernorm"""     )
  # text encoder
  if "text_encoder.token_embedding" in name:
    _lowerCAmelCase						:	Union[str, Any]        =					name.replace("""text_encoder.token_embedding"""      ,"""text_model.embeddings.token_embedding"""     )
  if "text_encoder.positional_embedding" in name:
    _lowerCAmelCase						:	int        =					name.replace("""text_encoder.positional_embedding"""      ,"""text_model.embeddings.position_embedding.weight"""     )
  if "text_encoder.transformer.resblocks." in name:
    _lowerCAmelCase						:	Optional[int]        =					name.replace("""text_encoder.transformer.resblocks."""      ,"""text_model.encoder.layers."""     )
  if "ln_1" in name:
    _lowerCAmelCase						:	Tuple        =					name.replace("""ln_1"""      ,"""layer_norm1"""     )
  if "ln_2" in name:
    _lowerCAmelCase						:	int        =					name.replace("""ln_2"""      ,"""layer_norm2"""     )
  if "c_fc" in name:
    _lowerCAmelCase						:	str        =					name.replace("""c_fc"""      ,"""fc1"""     )
  if "c_proj" in name:
    _lowerCAmelCase						:	List[Any]        =					name.replace("""c_proj"""      ,"""fc2"""     )
  if "text_encoder" in name:
    _lowerCAmelCase						:	Dict        =					name.replace("""text_encoder"""      ,"""text_model"""     )
  if "ln_final" in name:
    _lowerCAmelCase						:	Optional[Any]        =					name.replace("""ln_final"""      ,"""final_layer_norm"""     )
  # projection layers
  if "img_projector.linear_hidden." in name:
    _lowerCAmelCase						:	Union[str, Any]        =					name.replace("""img_projector.linear_hidden."""      ,"""visual_projection."""     )
  if "img_projector.linear_out." in name:
    _lowerCAmelCase						:	List[str]        =					name.replace("""img_projector.linear_out."""      ,"""visual_projection.3."""     )
  if "text_projector.linear_hidden" in name:
    _lowerCAmelCase						:	Tuple        =					name.replace("""text_projector.linear_hidden"""      ,"""text_projection"""     )
  if "text_projector.linear_out" in name:
    _lowerCAmelCase						:	List[Any]        =					name.replace("""text_projector.linear_out"""      ,"""text_projection.3"""     )

  return name





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Optional[Any]     )	->				List[Any]:
  for key in orig_state_dict.copy().keys():
    _lowerCAmelCase						:	Tuple        =					orig_state_dict.pop(_lowerCamelCase     )

    if "qkv" in key:
      # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      _lowerCAmelCase						:	Optional[Any]        =					key.split("""."""     )
      _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					int(key_split[2]     ), int(key_split[4]     )
      _lowerCAmelCase						:	Union[str, Any]        =					config.vision_config.hidden_size
      if "weight" in key:
        _lowerCAmelCase						:	str        =					val[:dim, :]
        _lowerCAmelCase						:	Union[str, Any]        =					val[dim : dim * 2, :]
        _lowerCAmelCase						:	Union[str, Any]        =					val[-dim:, :]
      else:
        _lowerCAmelCase						:	str        =					val[:dim]
        _lowerCAmelCase						:	Any        =					val[dim : dim * 2]
        _lowerCAmelCase						:	List[str]        =					val[-dim:]
    elif "in_proj" in key:
      # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      _lowerCAmelCase						:	Optional[int]        =					key.split("""."""     )
      _lowerCAmelCase						:	str        =					int(key_split[3]     )
      _lowerCAmelCase						:	Dict        =					config.text_config.hidden_size
      if "weight" in key:
        _lowerCAmelCase						:	Any        =					val[:dim, :]
        _lowerCAmelCase						:	int        =					val[
            dim : dim * 2, :
        ]
        _lowerCAmelCase						:	Optional[int]        =					val[-dim:, :]
      else:
        _lowerCAmelCase						:	Union[str, Any]        =					val[:dim]
        _lowerCAmelCase						:	int        =					val[dim : dim * 2]
        _lowerCAmelCase						:	Optional[int]        =					val[-dim:]
    else:
      _lowerCAmelCase						:	List[Any]        =					rename_key(_lowerCamelCase     )
      # squeeze if necessary
      if (
          "text_projection.0" in new_name
          or "text_projection.3" in new_name
          or "visual_projection.0" in new_name
          or "visual_projection.3" in new_name
      ):
        _lowerCAmelCase						:	Any        =					val.squeeze_()
      else:
        _lowerCAmelCase						:	Union[str, Any]        =					val

  return orig_state_dict





def 							SCREAMING_SNAKE_CASE					(      )	->				Tuple:
  _lowerCAmelCase						:	Tuple        =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _lowerCAmelCase						:	Dict        =					Image.open(requests.get(_lowerCamelCase      ,stream=_lowerCamelCase     ).raw     )
  return im





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Optional[int]="groupvit-gcc-yfcc"      ,_lowerCamelCase						:	List[Any]=False     )	->				Dict:
  _lowerCAmelCase						:	Tuple        =					GroupViTConfig()
  _lowerCAmelCase						:	Dict        =					GroupViTModel(_lowerCamelCase     ).eval()

  _lowerCAmelCase						:	Union[str, Any]        =					torch.load(_lowerCamelCase      ,map_location="""cpu"""     )["""model"""]
  _lowerCAmelCase						:	List[str]        =					convert_state_dict(_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					model.load_state_dict(_lowerCamelCase      ,strict=_lowerCamelCase     )
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCamelCase     ) == 0)

  # verify result
  _lowerCAmelCase						:	Tuple        =					CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
  _lowerCAmelCase						:	Dict        =					prepare_img()
  _lowerCAmelCase						:	Union[str, Any]        =					processor(text=["""a photo of a cat""", """a photo of a dog"""]      ,images=_lowerCamelCase      ,padding=_lowerCamelCase      ,return_tensors="""pt"""     )

  with torch.no_grad():
    _lowerCAmelCase						:	str        =					model(**_lowerCamelCase     )

  if model_name == "groupvit-gcc-yfcc":
    _lowerCAmelCase						:	str        =					torch.tensor([[13.35_23, 6.36_29]]     )
  elif model_name == "groupvit-gcc-redcaps":
    _lowerCAmelCase						:	Union[str, Any]        =					torch.tensor([[16.18_73, 8.62_30]]     )
  else:
    raise ValueError(f"Model name {model_name} not supported."     )
  assert torch.allclose(outputs.logits_per_image      ,_lowerCamelCase      ,atol=1e-3     )

  processor.save_pretrained(_lowerCamelCase     )
  model.save_pretrained(_lowerCamelCase     )
  print("""Successfully saved processor and model to"""      ,_lowerCamelCase     )

  if push_to_hub:
    print("""Pushing to the hub..."""     )
    processor.push_to_hub(_lowerCamelCase      ,organization="""nielsr"""     )
    model.push_to_hub(_lowerCamelCase      ,organization="""nielsr"""     )


if __name__ == "__main__":
     _a				:		str	   =       argparse.ArgumentParser()
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
     )
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
     parser.add_argument(
         '--model_name',
         default='groupvit-gccy-fcc',
         type=str,
         help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
     )
     _a				:		Union[str, Any]	   =       parser.parse_args()

     convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				str:
  _lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					checkpoints.load_tax_checkpoint(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _lowerCAmelCase						:	Tuple        =					"""SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _lowerCAmelCase						:	Optional[Any]        =					"""LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	Union[str, Any]        =					"""TransientGlobalSelfAttention"""
  else:
    raise ValueError(
        """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
        """ attribute with a value from ['local', 'transient-global]."""     )

  # Encoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Tuple        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Dict        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	int        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	Any        =					flax_model.params["""encoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	Any        =					tax_attention_key
    _lowerCAmelCase						:	str        =					tax_attention_out
    _lowerCAmelCase						:	Union[str, Any]        =					tax_attention_query
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_value

    _lowerCAmelCase						:	List[str]        =					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Any        =					tax_global_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	List[Any]        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	List[str]        =					tax_mlp_wi

    _lowerCAmelCase						:	str        =					tax_mlp_wo
    _lowerCAmelCase						:	Optional[Any]        =					tax_mlp_layer_norm

    _lowerCAmelCase						:	Any        =					flax_model_encoder_layer_block

  # Only for layer 0:
  _lowerCAmelCase						:	Union[str, Any]        =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Optional[Any]        =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _lowerCAmelCase						:	Optional[int]        =					tax_encoder_global_rel_embedding

  # Assigning
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	Any        =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Optional[int]        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	int        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

    # Encoder-Decoder-Attention
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""key"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_module["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Dict        =					tax_enc_dec_attention_module["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

    # MLP
    if split_mlp_wi:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	str        =					flax_model.params["""decoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	int        =					tax_attention_key
    _lowerCAmelCase						:	List[str]        =					tax_attention_out
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_query
    _lowerCAmelCase						:	Dict        =					tax_attention_value

    _lowerCAmelCase						:	str        =					tax_pre_attention_layer_norm

    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_key
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_out
    _lowerCAmelCase						:	Tuple        =					tax_enc_dec_attention_query
    _lowerCAmelCase						:	Any        =					tax_enc_dec_attention_value

    _lowerCAmelCase						:	Dict        =					tax_cross_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	int        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	Optional[int]        =					tax_mlp_wi

    _lowerCAmelCase						:	Dict        =					tax_mlp_wo

    _lowerCAmelCase						:	List[Any]        =					txa_mlp_layer_norm

    _lowerCAmelCase						:	Optional[Any]        =					flax_model_decoder_layer_block

  # Decoder Normalization
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	List[str]        =					txa_decoder_norm

  # Only for layer 0:
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Union[str, Any]        =					tax_decoder_rel_embedding

  # Token Embeddings
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  _lowerCAmelCase						:	Optional[int]        =					txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
     _a				:		Any	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/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)




