
'''simple docstring'''






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


UpperCAmelCase_       :   List[Any]		=       [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
UpperCAmelCase_       :   Union[str, Any]		=       [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

UpperCAmelCase_       :   List[Any]		=       (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCAmelCase_       :   Optional[int]		=       (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCAmelCase_       :   Tuple		=       [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      for tf_name, hf_name in patterns:
            _lowerCamelCase				:   int								=   k.replace(_lowerCAmelCase				,	_lowerCAmelCase							)
      return k







def 		A_       (	_lowerCAmelCase   :    dict				,	_lowerCAmelCase   :    dict							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   BigBirdPegasusConfig(**_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   BigBirdPegasusForConditionalGeneration(_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   torch_model.state_dict()
      _lowerCamelCase				:   Optional[int]								=   {}

      # separating decoder weights
      _lowerCamelCase				:   Tuple								=   {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"							)}
      _lowerCamelCase				:   int								=   {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"							)}

      for k, v in tqdm(decoder_weights.items()				,	"tf -> hf conversion"							):
            _lowerCamelCase				:   Any								=   [k.endswith(_lowerCAmelCase							) for ending in KEYS_TO_IGNORE]
            if any(_lowerCAmelCase							):
                  continue
            _lowerCamelCase				:   Any								=   DECODER_PATTERNS
            _lowerCamelCase				:   List[str]								=   rename_state_dict_key(_lowerCAmelCase				,	_lowerCAmelCase							)
            if new_k not in state_dict:
                  raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]							):
                  _lowerCamelCase				:   Union[str, Any]								=   v.T
            _lowerCamelCase				:   List[Any]								=   torch.from_numpy(_lowerCAmelCase							)
            assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

      for k, v in tqdm(remaining_weights.items()				,	"tf -> hf conversion"							):
            _lowerCamelCase				:   Optional[int]								=   [k.endswith(_lowerCAmelCase							) for ending in KEYS_TO_IGNORE]
            if any(_lowerCAmelCase							):
                  continue
            _lowerCamelCase				:   str								=   REMAINING_PATTERNS
            _lowerCamelCase				:   str								=   rename_state_dict_key(_lowerCAmelCase				,	_lowerCAmelCase							)
            if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
                  raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]							):
                  _lowerCamelCase				:   int								=   v.T
            _lowerCamelCase				:   Optional[Any]								=   torch.from_numpy(_lowerCAmelCase							)
            if k != "pegasus/embeddings/position_embeddings":
                  assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

      _lowerCamelCase				:   Dict								=   mapping["model.embed_positions.weight"]
      _lowerCamelCase				:   Union[str, Any]								=   mapping.pop("model.embed_positions.weight"							)
      _lowerCamelCase				:   List[str]								=   torch_model.load_state_dict(_lowerCAmelCase				,	strict=_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   [
          k
          for k in missing
          if k
          not in [
              "final_logits_bias",
              "model.encoder.embed_tokens.weight",
              "model.decoder.embed_tokens.weight",
              "lm_head.weight",
          ]
      ]
      assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
      assert extra == [], F'no matches found for the following tf keys {extra}'
      return torch_model







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[Any]								=   tf.train.list_variables(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   List[Any]								=   ["global_step"]
      for name, shape in tqdm(_lowerCAmelCase				,	desc="converting tf checkpoint to dict"							):
            _lowerCamelCase				:   List[Any]								=   any(pat in name for pat in ignore_name							)
            if skip_key:
                  continue
            _lowerCamelCase				:   Any								=   tf.train.load_variable(_lowerCAmelCase				,	_lowerCAmelCase							)
            _lowerCamelCase				:   List[Any]								=   array
      return tf_weights







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    dict							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   get_tf_weights_as_numpy(_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   convert_bigbird_pegasus(_lowerCAmelCase				,	_lowerCAmelCase							)
      torch_model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Dict		=       argparse.ArgumentParser()
 parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
 parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
 UpperCAmelCase_       :   str		=       parser.parse_args()
 UpperCAmelCase_       :   Dict		=       {}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)

'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




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

      return name







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase				:   Optional[int]								=   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				:   Any								=   key.split("."							)
                  _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   int(key_split[2]							), int(key_split[4]							)
                  _lowerCamelCase				:   List[Any]								=   config.vision_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   int								=   val[dim : dim * 2, :]
                        _lowerCamelCase				:   str								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   Optional[int]								=   val[:dim]
                        _lowerCamelCase				:   str								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Tuple								=   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[Any]								=   key.split("."							)
                  _lowerCamelCase				:   int								=   int(key_split[3]							)
                  _lowerCamelCase				:   Tuple								=   config.text_config.hidden_size
                  if "weight" in key:
                        _lowerCamelCase				:   List[Any]								=   val[:dim, :]
                        _lowerCamelCase				:   str								=   val[
                            dim : dim * 2, :
                        ]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:, :]
                  else:
                        _lowerCamelCase				:   str								=   val[:dim]
                        _lowerCamelCase				:   Optional[Any]								=   val[dim : dim * 2]
                        _lowerCamelCase				:   Optional[int]								=   val[-dim:]
            else:
                  _lowerCamelCase				:   int								=   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 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   "http://images.cocodataset.org/val2017/000000039769.jpg"
      _lowerCamelCase				:   Any								=   Image.open(requests.get(_lowerCAmelCase				,	stream=_lowerCAmelCase							).raw							)
      return im







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    str="groupvit-gcc-yfcc"				,	_lowerCAmelCase   :    List[str]=False							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   GroupViTConfig()
      _lowerCamelCase				:   Union[str, Any]								=   GroupViTModel(_lowerCAmelCase							).eval()

      _lowerCamelCase				:   Any								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]
      _lowerCamelCase				:   Union[str, Any]								=   convert_state_dict(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   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				:   List[Any]								=   CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
      _lowerCamelCase				:   List[str]								=   prepare_img()
      _lowerCamelCase				:   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				:   List[str]								=   model(**_lowerCAmelCase							)

      if model_name == "groupvit-gcc-yfcc":
            _lowerCamelCase				:   List[Any]								=   torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]							)
      elif model_name == "groupvit-gcc-redcaps":
            _lowerCamelCase				:   Optional[Any]								=   torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]							)
      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__":
 UpperCAmelCase_       :   Optional[int]   =       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`.',
 )
 UpperCAmelCase_       :   Union[str, Any]   =       parser.parse_args()

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