'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

  return name

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for key in orig_state_dict.copy().keys():
    lowercase_     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE_						)

    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
      lowercase_     =       key.split("""."""						)
      lowercase_     ,	lowercase_     =       int(key_split[2]						), int(key_split[4]						)
      lowercase_     =       config.vision_config.hidden_size
      if "weight" in key:
        lowercase_     =       val[:dim, :]
        lowercase_     =       val[dim : dim * 2, :]
        lowercase_     =       val[-dim:, :]
      else:
        lowercase_     =       val[:dim]
        lowercase_     =       val[dim : dim * 2]
        lowercase_     =       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
      lowercase_     =       key.split("""."""						)
      lowercase_     =       int(key_split[3]						)
      lowercase_     =       config.text_config.hidden_size
      if "weight" in key:
        lowercase_     =       val[:dim, :]
        lowercase_     =       val[
            dim : dim * 2, :
        ]
        lowercase_     =       val[-dim:, :]
      else:
        lowercase_     =       val[:dim]
        lowercase_     =       val[dim : dim * 2]
        lowercase_     =       val[-dim:]
    else:
      lowercase_     =       rename_key(SCREAMING_SNAKE_CASE_						)
      # 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
      ):
        lowercase_     =       val.squeeze_()
      else:
        lowercase_     =       val

  return orig_state_dict

def   A_      (       )			->List[str]:
  lowercase_     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase_     =       Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,	stream=SCREAMING_SNAKE_CASE_						).raw						)
  return im

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_="groupvit-gcc-yfcc" ,	SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     =       GroupViTConfig()
  lowercase_     =       GroupViTModel(SCREAMING_SNAKE_CASE_						).eval()

  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)["""model"""]
  lowercase_     =       convert_state_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE_						) == 0)

  # verify result
  lowercase_     =       CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""						)
  lowercase_     =       prepare_img()
  lowercase_     =       processor(text=["""a photo of a cat""", """a photo of a dog"""] ,	images=SCREAMING_SNAKE_CASE_ ,	padding=SCREAMING_SNAKE_CASE_ ,	return_tensors="""pt"""						)

  with torch.no_grad():
    lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)

  if model_name == "groupvit-gcc-yfcc":
    lowercase_     =       torch.tensor([[13.35_23, 6.36_29]]						)
  elif model_name == "groupvit-gcc-redcaps":
    lowercase_     =       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 ,	SCREAMING_SNAKE_CASE_ ,	atol=1e-3						)

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

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


if __name__ == "__main__":
       __snake_case						  =		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`.""",
       )
       __snake_case						  =		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

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """encoder.version""",
      """decoder.version""",
      """model.encoder.version""",
      """model.decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """encoder.embed_positions._float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE_ ,	max_source_positions=args.max_source_positions ,	max_target_positions=args.max_target_positions ,	encoder_layers=args.encoder_layers ,	decoder_layers=args.decoder_layers ,	encoder_attention_heads=args.encoder_attention_heads ,	decoder_attention_heads=args.decoder_attention_heads ,	encoder_ffn_dim=args.encoder_ffn_embed_dim ,	decoder_ffn_dim=args.decoder_ffn_embed_dim ,	d_model=args.encoder_embed_dim ,	dropout=args.dropout ,	attention_dropout=args.attention_dropout ,	activation_dropout=args.activation_dropout ,	activation_function="""relu""" ,	num_conv_layers=len(SCREAMING_SNAKE_CASE_						) ,	conv_channels=args.conv_channels ,	conv_kernel_sizes=SCREAMING_SNAKE_CASE_ ,	input_feat_per_channel=args.input_feat_per_channel ,	input_channels=args.input_channels ,	tie_word_embeddings=SCREAMING_SNAKE_CASE_ ,	num_beams=5 ,	max_length=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       model.model.load_state_dict(SCREAMING_SNAKE_CASE_ ,	strict=SCREAMING_SNAKE_CASE_						)
  if len(SCREAMING_SNAKE_CASE_						) > 0 and not set(SCREAMING_SNAKE_CASE_						) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
        f""" but all the following weights are missing {missing}"""						)

  if tie_embeds:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
       parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       __snake_case						  =		parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

