

'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Any:
       if "cls_token" in name:
              A_						=      name.replace("""cls_token""",       """vit.embeddings.cls_token"""				)
       if "mask_token" in name:
              A_						=      name.replace("""mask_token""",       """decoder.mask_token"""				)
       if "decoder_pos_embed" in name:
              A_						=      name.replace("""decoder_pos_embed""",       """decoder.decoder_pos_embed"""				)
       if "pos_embed" in name and "decoder" not in name:
              A_						=      name.replace("""pos_embed""",       """vit.embeddings.position_embeddings"""				)
       if "patch_embed.proj" in name:
              A_						=      name.replace("""patch_embed.proj""",       """vit.embeddings.patch_embeddings.projection"""				)
       if "patch_embed.norm" in name:
              A_						=      name.replace("""patch_embed.norm""",       """vit.embeddings.norm"""				)
       if "decoder_blocks" in name:
              A_						=      name.replace("""decoder_blocks""",       """decoder.decoder_layers"""				)
       if "blocks" in name:
              A_						=      name.replace("""blocks""",       """vit.encoder.layer"""				)
       if "attn.proj" in name:
              A_						=      name.replace("""attn.proj""",       """attention.output.dense"""				)
       if "attn" in name:
              A_						=      name.replace("""attn""",       """attention.self"""				)
       if "norm1" in name:
              A_						=      name.replace("""norm1""",       """layernorm_before"""				)
       if "norm2" in name:
              A_						=      name.replace("""norm2""",       """layernorm_after"""				)
       if "mlp.fc1" in name:
              A_						=      name.replace("""mlp.fc1""",       """intermediate.dense"""				)
       if "mlp.fc2" in name:
              A_						=      name.replace("""mlp.fc2""",       """output.dense"""				)
       if "decoder_embed" in name:
              A_						=      name.replace("""decoder_embed""",       """decoder.decoder_embed"""				)
       if "decoder_norm" in name:
              A_						=      name.replace("""decoder_norm""",       """decoder.decoder_norm"""				)
       if "decoder_pred" in name:
              A_						=      name.replace("""decoder_pred""",       """decoder.decoder_pred"""				)
       if "norm.weight" in name and "decoder" not in name:
              A_						=      name.replace("""norm.weight""",       """vit.layernorm.weight"""				)
       if "norm.bias" in name and "decoder" not in name:
              A_						=      name.replace("""norm.bias""",       """vit.layernorm.bias"""				)

       return name
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Union[str, Any]:
       for key in orig_state_dict.copy().keys():
              A_						=      orig_state_dict.pop(UpperCAmelCase__				)

              if "qkv" in key:
                     A_						=      key.split("""."""				)
                     A_						=      int(key_split[1]				)
                     if "decoder_blocks" in key:
                            A_						=      config.decoder_hidden_size
                            A_						=      """decoder.decoder_layers."""
                            if "weight" in key:
                                   A_						=      val[:dim, :]
                                   A_						=      val[dim : dim * 2, :]
                                   A_						=      val[-dim:, :]
                            elif "bias" in key:
                                   A_						=      val[:dim]
                                   A_						=      val[dim : dim * 2]
                                   A_						=      val[-dim:]
                     else:
                            A_						=      config.hidden_size
                            A_						=      """vit.encoder.layer."""
                            if "weight" in key:
                                   A_						=      val[:dim, :]
                                   A_						=      val[dim : dim * 2, :]
                                   A_						=      val[-dim:, :]
                            elif "bias" in key:
                                   A_						=      val[:dim]
                                   A_						=      val[dim : dim * 2]
                                   A_						=      val[-dim:]

              else:
                     A_						=      val

       return orig_state_dict
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[Any]:
       A_						=      ViTMAEConfig()
       if "large" in checkpoint_url:
              A_						=      10_24
              A_						=      40_96
              A_						=      24
              A_						=      16
       elif "huge" in checkpoint_url:
              A_						=      14
              A_						=      12_80
              A_						=      51_20
              A_						=      32
              A_						=      16

       A_						=      ViTMAEForPreTraining(UpperCAmelCase__				)

       A_						=      torch.hub.load_state_dict_from_url(UpperCAmelCase__,       map_location="""cpu"""				)["""model"""]

       A_						=      ViTMAEImageProcessor(size=config.image_size				)

       A_						=      convert_state_dict(UpperCAmelCase__,       UpperCAmelCase__				)

       model.load_state_dict(UpperCAmelCase__				)
       model.eval()

       A_						=      """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

       A_						=      Image.open(requests.get(UpperCAmelCase__,       stream=UpperCAmelCase__				).raw				)
       A_						=      ViTMAEImageProcessor(size=config.image_size				)
       A_						=      image_processor(images=UpperCAmelCase__,       return_tensors="""pt"""				)

       # forward pass
       torch.manual_seed(2				)
       A_						=      model(**UpperCAmelCase__				)
       A_						=      outputs.logits

       if "large" in checkpoint_url:
              A_						=      torch.tensor(
                  [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
       elif "huge" in checkpoint_url:
              A_						=      torch.tensor(
                  [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
       else:
              A_						=      torch.tensor(
                  [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

       # verify logits
       assert torch.allclose(logits[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				)

       print(F'''Saving model to {pytorch_dump_folder_path}'''				)
       model.save_pretrained(UpperCAmelCase__				)

       print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
       image_processor.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
          type=str,
          help='''URL of the checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      __lowerCamelCase		       =    parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCamelCase		       =    {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Tuple:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       A_						=      list(s_dict.keys()				)
       for key in keys:
              A_						=      r""".*/layers_(\d+)"""
              A_						=      key
              if re.match(UpperCAmelCase__,       UpperCAmelCase__				):
                     A_						=      re.sub(r"""layers_(\d+)""",       r"""block/\1/layer""",       UpperCAmelCase__				)

              A_						=      r"""(encoder|decoder)\/"""

              if re.match(UpperCAmelCase__,       UpperCAmelCase__				):
                     A_						=      re.match(UpperCAmelCase__,       UpperCAmelCase__				).groups()
                     if groups[0] == "encoder":
                            A_						=      re.sub(r"""/mlp/""",       r"""/1/mlp/""",       UpperCAmelCase__				)
                            A_						=      re.sub(r"""/pre_mlp_layer_norm/""",       r"""/1/layer_norm/""",       UpperCAmelCase__				)

                     elif groups[0] == "decoder":
                            A_						=      re.sub(r"""/mlp/""",       r"""/2/mlp/""",       UpperCAmelCase__				)
                            A_						=      re.sub(r"""/pre_mlp_layer_norm/""",       r"""/2/layer_norm/""",       UpperCAmelCase__				)

        # 2. Convert other classic mappings
              for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                     if old_key in new_key:
                            A_						=      new_key.replace(UpperCAmelCase__,       UpperCAmelCase__				)

              print(F'''{key} -> {new_key}'''				)
              A_						=      s_dict.pop(UpperCAmelCase__				)

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              A_						=      s_dict[
                  """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
              ].T
       if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              A_						=      s_dict[
                  """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
              ].T

       # 3. Take extra care of the EXPERTS layer
       for key in list(s_dict.keys()				):
              if "expert" in key:
                     A_						=      s_dict[key].shape[0]
                     A_						=      s_dict[key]
                     for idx in range(UpperCAmelCase__				):
                            A_						=      expert_weihts[idx]
                            print(F'''{key} -> {key.replace("expert/",       "nested fstring"				)}'''				)

                     s_dict.pop(UpperCAmelCase__				)

       return s_dict


__lowerCamelCase		       =    {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(UpperCAmelCase__,       """r"""				) as f:
              A_						=      f.read()

       A_						=      re.findall(r"""(.*) = ([0-9.]*)""",       UpperCAmelCase__				)
       A_						=      {}
       for param, value in regex_match:
              if param in GIN_TO_CONFIG_MAPPING and value != "":
                     A_						=      float(UpperCAmelCase__				) if """.""" in value else int(UpperCAmelCase__				)

       A_						=      re.findall(r"""(.*activations) = \(\'(.*)\',\)""",       UpperCAmelCase__				)[0]
       A_						=      str(activation[1]				)

       A_						=      num_experts
       A_						=      SwitchTransformersConfig(**UpperCAmelCase__				)
       return config
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__="./",       UpperCAmelCase__=8				)			->			List[str]:
       # Initialise PyTorch model

       print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
       A_						=      checkpoints.load_tax_checkpoint(UpperCAmelCase__				)

       if gin_file is not None:
              A_						=      convert_gin_to_config(UpperCAmelCase__,       UpperCAmelCase__				)
       else:
              A_						=      SwitchTransformersConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      SwitchTransformersForConditionalGeneration(UpperCAmelCase__				)

       A_						=      flax_params["""target"""]
       A_						=      flatten_dict(UpperCAmelCase__,       sep="""/"""				)
       A_						=      rename_keys(UpperCAmelCase__				)
       A_						=      unflatten_dict(UpperCAmelCase__,       sep="""/"""				)

       # Load the flax params in the PT model
       load_flax_weights_in_pytorch_model(UpperCAmelCase__,       UpperCAmelCase__				)

       print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
       pt_model.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--switch_t5x_checkpoint_path''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
              ''' model architecture. If not provided, a `gin_file` has to be provided.'''
          ),
      )
      parser.add_argument(
          '''--gin_file''',
          default=None,
          type=str,
          required=False,
          help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
      )
      parser.add_argument(
          '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
      )
      parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
      __lowerCamelCase		       =    parser.parse_args()
      convert_flax_checkpoint_to_pytorch(
          args.switch_tax_checkpoint_path,
          args.config_name,
          args.gin_file,
          args.pytorch_dump_folder_path,
          args.num_experts,
      )
