


"""simple docstring"""






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]
def       A_   (   _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Optional[Any]="attention"       ):






     """simple docstring"""

     _a    	=				_a    	=				np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]       )
     _a    	=				k_tmp.reshape(k_tmp.shape[0],     k_tmp.shape[1] * k_tmp.shape[2]       )
     _a    	=				np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]       )
     _a    	=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1],     o_tmp.shape[2]       )
     _a    	=				np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]       )
     _a    	=				q_tmp.reshape(q_tmp.shape[0],     q_tmp.shape[1] * q_tmp.shape[2]       )
     _a    	=				np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]       )
     _a    	=				v_tmp.reshape(v_tmp.shape[0],     v_tmp.shape[1] * v_tmp.shape[2]       )
     return k, o, q, v
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     if split_mlp_wi:
          _a    	=				params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
          _a    	=				params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
          _a    	=				(wi_a, wi_a)
     else:
          _a    	=				params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

     _a    	=				params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
     return wi, wo
def       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int],     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]
def       A_   (   _lowerCAmelCase     :					dict,     *, _lowerCAmelCase     :					int,     _lowerCAmelCase     :					bool,     _lowerCAmelCase     :					bool = False       ):






     """simple docstring"""

     _a    	=				traverse_util.flatten_dict(variables['''target''']       )
     _a    	=				{'''/'''.join(_lowerCAmelCase       ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     _a    	=				'''encoder/encoder/mlp/wi_0/kernel''' in old
     print('''Split MLP:''',     _lowerCAmelCase       )

     _a    	=				collections.OrderedDict()

     # Shared embeddings.
     _a    	=				old['''token_embedder/embedding''']

     # Encoder.
     for i in range(_lowerCAmelCase       ):
          # Block i, layer 0 (Self Attention).
          _a    	=				tax_layer_norm_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''encoder''',     '''pre_attention_layer_norm'''       )
          _a	,      _a	,      _a	,      _a    	=				tax_attention_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''encoder''',     '''attention'''       )
          _a    	=				layer_norm
          _a    	=				k.T
          _a    	=				o.T
          _a    	=				q.T
          _a    	=				v.T

          # Block i, layer 1 (MLP).
          _a    	=				tax_layer_norm_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''encoder''',     '''pre_mlp_layer_norm'''       )
          _a	,      _a    	=				tax_mlp_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''encoder''',     _lowerCAmelCase       )
          _a    	=				layer_norm
          if split_mlp_wi:
               _a    	=				wi[0].T
               _a    	=				wi[1].T
          else:
               _a    	=				wi.T
          _a    	=				wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               _a    	=				tax_relpos_bias_lookup(
                   _lowerCAmelCase,     _lowerCAmelCase,     '''encoder'''       ).T

     _a    	=				old['''encoder/encoder_norm/scale''']

     if not scalable_attention:
          _a    	=				tax_relpos_bias_lookup(
              _lowerCAmelCase,     0,     '''encoder'''       ).T
          _a    	=				tax_relpos_bias_lookup(
              _lowerCAmelCase,     0,     '''decoder'''       ).T

     if not is_encoder_only:
          # Decoder.
          for i in range(_lowerCAmelCase       ):
               # Block i, layer 0 (Self Attention).
               _a    	=				tax_layer_norm_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     '''pre_self_attention_layer_norm'''       )
               _a	,      _a	,      _a	,      _a    	=				tax_attention_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     '''self_attention'''       )
               _a    	=				layer_norm
               _a    	=				k.T
               _a    	=				o.T
               _a    	=				q.T
               _a    	=				v.T

               # Block i, layer 1 (Cross Attention).
               _a    	=				tax_layer_norm_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     '''pre_cross_attention_layer_norm'''       )
               _a	,      _a	,      _a	,      _a    	=				tax_attention_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     '''encoder_decoder_attention'''       )
               _a    	=				layer_norm
               _a    	=				k.T
               _a    	=				o.T
               _a    	=				q.T
               _a    	=				v.T

               # Block i, layer 2 (MLP).
               _a    	=				tax_layer_norm_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     '''pre_mlp_layer_norm'''       )
               _a	,      _a    	=				tax_mlp_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder''',     _lowerCAmelCase       )
               _a    	=				layer_norm
               if split_mlp_wi:
                    _a    	=				wi[0].T
                    _a    	=				wi[1].T
               else:
                    _a    	=				wi.T
               _a    	=				wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    _a    	=				tax_relpos_bias_lookup(_lowerCAmelCase,     _lowerCAmelCase,     '''decoder'''       ).T

          _a    	=				old['''decoder/decoder_norm/scale''']

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               _a    	=				old['''decoder/logits_dense/kernel'''].T

     return new
def       A_   (   _lowerCAmelCase     :					Optional[int],     _lowerCAmelCase     :					bool       ):






     """simple docstring"""

     _a    	=				collections.OrderedDict([(k, torch.from_numpy(v.copy()       )) for (k, v) in converted_params.items()]       )

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          _a    	=				state_dict['''shared.weight''']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               _a    	=				state_dict['''shared.weight''']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('''Using shared word embeddings as lm_head.'''       )
               _a    	=				state_dict['''shared.weight''']

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






     """simple docstring"""

     _a    	=				checkpoints.load_tax_checkpoint(_lowerCAmelCase       )
     _a    	=				convert_tax_to_pytorch(
         _lowerCAmelCase,     num_layers=config.num_layers,     is_encoder_only=_lowerCAmelCase,     scalable_attention=_lowerCAmelCase       )
     _a    	=				make_state_dict(_lowerCAmelCase,     _lowerCAmelCase       )
     model.load_state_dict(_lowerCAmelCase,     strict=_lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					bool = False,     _lowerCAmelCase     :					bool = False,     ):






     """simple docstring"""

     _a    	=				MTaConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          _a    	=				UMTaEncoderModel(_lowerCAmelCase       )
     else:
          _a    	=				UMTaForConditionalGeneration(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(_lowerCAmelCase       )

     # Verify that we can load the checkpoint.
     model.from_pretrained(_lowerCAmelCase       )
     print('''Done'''       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   parser.add_argument(
       '''--scalable_attention''',
       action='''store_true''',
       help='''Whether the model uses scaled attention (umt5 model)''',
       default=False,
   )
   __snake_case		=       parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

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

     return name
def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _a    	=				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
               _a    	=				key.split('''.'''       )
               _a	,      _a    	=				int(key_split[2]       ), int(key_split[4]       )
               _a    	=				config.vision_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[dim : dim * 2, :]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				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
               _a    	=				key.split('''.'''       )
               _a    	=				int(key_split[3]       )
               _a    	=				config.text_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[
                        dim : dim * 2, :
                    ]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          else:
               _a    	=				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
               ):
                    _a    	=				val.squeeze_()
               else:
                    _a    	=				val

     return orig_state_dict
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict="groupvit-gcc-yfcc",     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				GroupViTConfig()
     _a    	=				GroupViTModel(_lowerCAmelCase       ).eval()

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     _a    	=				convert_state_dict(_lowerCAmelCase,     _lowerCAmelCase       )
     _a	,      _a    	=				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
     _a    	=				CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
     _a    	=				prepare_img()
     _a    	=				processor(text=['''a photo of a cat''', '''a photo of a dog'''],     images=_lowerCAmelCase,     padding=_lowerCAmelCase,     return_tensors='''pt'''       )

     with torch.no_grad():
          _a    	=				model(**_lowerCAmelCase       )

     if model_name == "groupvit-gcc-yfcc":
          _a    	=				torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
     elif model_name == "groupvit-gcc-redcaps":
          _a    	=				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__":
   __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)