





'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   for key in orig_state_dict.copy().keys():
      lowercase_      :Optional[Any]         =			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
         lowercase_      :int         =			key.split("."			)
         lowercase_      ,							lowercase_      :List[Any]         =			int(key_split[2]			), int(key_split[4]			)
         lowercase_      :Optional[Any]         =			config.vision_config.hidden_size
         if "weight" in key:
            lowercase_      :Tuple         =			val[:dim, :]
            lowercase_      :Optional[int]         =			val[dim : dim * 2, :]
            lowercase_      :Any         =			val[-dim:, :]
         else:
            lowercase_      :int         =			val[:dim]
            lowercase_      :Tuple         =			val[dim : dim * 2]
            lowercase_      :int         =			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_      :Union[str, Any]         =			key.split("."			)
         lowercase_      :str         =			int(key_split[3]			)
         lowercase_      :int         =			config.text_config.hidden_size
         if "weight" in key:
            lowercase_      :Optional[int]         =			val[:dim, :]
            lowercase_      :Optional[Any]         =			val[
                dim : dim * 2, :
            ]
            lowercase_      :Tuple         =			val[-dim:, :]
         else:
            lowercase_      :List[str]         =			val[:dim]
            lowercase_      :str         =			val[dim : dim * 2]
            lowercase_      :Union[str, Any]         =			val[-dim:]
      else:
         lowercase_      :Dict         =			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
         ):
            lowercase_      :Optional[int]         =			val.squeeze_()
         else:
            lowercase_      :Optional[Any]         =			val

   return orig_state_dict
def 							UpperCAmelCase_  (						):
   lowercase_      :str         =			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_      :int         =			Image.open(requests.get(__lowerCamelCase     ,stream=__lowerCamelCase			).raw			)
   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple     ,__lowerCamelCase : str     ,__lowerCamelCase : int="groupvit-gcc-yfcc"     ,__lowerCamelCase : List[Any]=False			):
   lowercase_      :Union[str, Any]         =			GroupViTConfig()
   lowercase_      :Tuple         =			GroupViTModel(__lowerCamelCase			).eval()

   lowercase_      :Any         =			torch.load(__lowerCamelCase     ,map_location="cpu"			)["model"]
   lowercase_      :Optional[Any]         =			convert_state_dict(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      ,							lowercase_      :Any         =			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
   lowercase_      :Union[str, Any]         =			CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"			)
   lowercase_      :str         =			prepare_img()
   lowercase_      :Any         =			processor(text=["a photo of a cat", "a photo of a dog"]     ,images=__lowerCamelCase     ,padding=__lowerCamelCase     ,return_tensors="pt"			)

   with torch.no_grad():
      lowercase_      :int         =			model(**__lowerCamelCase			)

   if model_name == "groupvit-gcc-yfcc":
      lowercase_      :Dict         =			torch.tensor([[13.3_523, 6.3_629]]			)
   elif model_name == "groupvit-gcc-redcaps":
      lowercase_      :List[Any]         =			torch.tensor([[16.1_873, 8.6_230]]			)
   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__":
 lowerCAmelCase :					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`.''',
 )
 lowerCAmelCase :					int 							=parser.parse_args()

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









'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Union[tf.Tensor, np.ndarray]			):
   if isinstance(__lowerCamelCase     ,np.ndarray			):
      return list(tensor.shape			)

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)

   if tensor.shape == tf.TensorShape(__lowerCamelCase			):
      return dynamic

   lowercase_      :Union[str, Any]         =			tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase			)]
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : Optional[int] = None     ,__lowerCamelCase : Optional[str] = None			):
   return tf.nn.softmax(logits=logits + 1e-9     ,axis=__lowerCamelCase     ,name=__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any     ,__lowerCamelCase : List[str]=1e-5     ,__lowerCamelCase : List[str]=-1			):
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase     ,__lowerCamelCase			):
      raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

   # Get mean and variance on the axis to be normalized
   lowercase_      ,							lowercase_      :List[str]         =			tf.nn.moments(__lowerCamelCase     ,axes=[axis]     ,keepdims=__lowerCamelCase			)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      lowercase_      :Union[str, Any]         =			[1] * inputs.shape.rank
      lowercase_      :Optional[Any]         =			shape_list(__lowerCamelCase			)[axis]
      lowercase_      :List[str]         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :Dict         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)

   # Compute layer normalization using the batch_normalization
   # function.
   lowercase_      :Union[str, Any]         =			tf.nn.batch_normalization(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,offset=__lowerCamelCase     ,scale=__lowerCamelCase     ,variance_epsilon=__lowerCamelCase     ,)
   return outputs
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Union[str, Any]=0     ,__lowerCamelCase : Dict=-1			):
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)
   lowercase_      :Optional[int]         =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
   lowercase_      :List[str]         =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,axis=0			)
   return tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor			):
   if not isinstance(__lowerCamelCase     ,tf.Tensor			):
      lowercase_      :str         =			tf.convert_to_tensor(__lowerCamelCase			)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      lowercase_      :List[Any]         =			encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      lowercase_      :Optional[int]         =			encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   lowercase_      :str         =			(
       tf.cast(1     ,encoder_attention_mask.dtype			) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : int     ,__lowerCamelCase : str = "input_ids"			):
   tf.debugging.assert_less(
       __lowerCamelCase     ,tf.cast(__lowerCamelCase     ,dtype=tensor.dtype			)     ,message=(
           F'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase			)}) must be smaller than the embedding '
           F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
       )     ,)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Dict			):
   lowercase_      :int         =			6_45_12
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   lowercase_      :Union[str, Any]         =			[x for x in data if len(__lowerCamelCase			) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          "The following attributes cannot be saved to HDF5 file because "
          F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
          F'bytes: {bad_attributes}'			)

   lowercase_      :Union[str, Any]         =			np.asarray(__lowerCamelCase			)

   lowercase_      :Optional[int]         =			1
   lowercase_      :int         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
      num_chunks += 1
      lowercase_      :List[Any]         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(__lowerCamelCase			):
         lowercase_      :int         =			chunk_data
   else:
      lowercase_      :Tuple         =			data
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   if name in group.attrs:
      lowercase_      :Optional[Any]         =			[n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs[name]]
   else:
      lowercase_      :List[str]         =			[]
      lowercase_      :str         =			0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
         chunk_id += 1
   return data


def 							UpperCAmelCase_  (						__lowerCamelCase : str			):

   def _expand_single_ad_tensor(__lowerCamelCase : Tuple			):
      if isinstance(__lowerCamelCase     ,tf.Tensor			) and t.shape.rank == 1:
         return tf.expand_dims(__lowerCamelCase     ,axis=-1			)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor     ,__lowerCamelCase			)


