

'''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,
      )



'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Load configuration defined in the metadata file
       with open(UpperCAmelCase__				) as metadata_file:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      LukeConfig(use_entity_aware_attention=UpperCAmelCase__,       **metadata["""model_config"""]				)

       # Load in the weights from the checkpoint_path
       A_						=      torch.load(UpperCAmelCase__,       map_location="""cpu"""				)["""module"""]

       # Load the entity vocab file
       A_						=      load_original_entity_vocab(UpperCAmelCase__				)
       # add an entry for [MASK2]
       A_						=      max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       A_						=      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

       # Add special tokens to the token vocabulary for downstream tasks
       A_						=      AddedToken("""<ent>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       A_						=      AddedToken("""<ent2>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}				)
       config.vocab_size += 2

       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''				)
       tokenizer.save_pretrained(UpperCAmelCase__				)
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """r"""				) as f:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      """MLukeTokenizer"""
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       with open(os.path.join(UpperCAmelCase__,       MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)

       # Initialize the embeddings of the special tokens
       A_						=      tokenizer.convert_tokens_to_ids(["""@"""]				)[0]
       A_						=      tokenizer.convert_tokens_to_ids(["""#"""]				)[0]

       A_						=      state_dict["""embeddings.word_embeddings.weight"""]
       A_						=      word_emb[ent_init_index].unsqueeze(0				)
       A_						=      word_emb[enta_init_index].unsqueeze(0				)
       A_						=      torch.cat([word_emb, ent_emb, enta_emb]				)
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              A_						=      state_dict[bias_name]
              A_						=      decoder_bias[ent_init_index].unsqueeze(0				)
              A_						=      decoder_bias[enta_init_index].unsqueeze(0				)
              A_						=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

       # Initialize the query layers of the entity-aware self-attention mechanism
       for layer_index in range(config.num_hidden_layers				):
              for matrix_name in ["query.weight", "query.bias"]:
                     A_						=      F'''encoder.layer.{layer_index}.attention.self.'''
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       A_						=      state_dict["""entity_embeddings.entity_embeddings.weight"""]
       A_						=      entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_emb, entity_mask_emb]				)
       # add [MASK2] for 'entity_predictions.bias'
       A_						=      state_dict["""entity_predictions.bias"""]
       A_						=      entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_prediction_bias, entity_mask_bias]				)

       A_						=      LukeForMaskedLM(config=UpperCAmelCase__				).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.bias"""				)
       A_						=      OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""				) or key.startswith("""entity_predictions"""				)):
                     A_						=      state_dict[key]
              else:
                     A_						=      state_dict[key]

       A_					,			A_						=      model.load_state_dict(UpperCAmelCase__,       strict=UpperCAmelCase__				)

       if set(UpperCAmelCase__				) != {"luke.embeddings.position_ids"}:
              raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
       if set(UpperCAmelCase__				) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''				)

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__,       task="""entity_classification"""				)

       A_						=      """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       A_						=      (0, 9)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 33, 7_68)				)
              A_						=      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

       if not (outputs.last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''				)
       if not torch.allclose(outputs.last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 1, 7_68)				)
              A_						=      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

       if not (outputs.entity_last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                  F''' {expected_shape}'''				)
       if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify masked word/entity prediction
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)
       A_						=      """Tokyo is the capital of <mask>."""
       A_						=      (24, 30)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       A_						=      encoding["""input_ids"""][0].tolist()
       A_						=      input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""				)				)
       A_						=      outputs.logits[0][mask_position_id].argmax(dim=-1				)
       assert "Japan" == tokenizer.decode(UpperCAmelCase__				)

       A_						=      outputs.entity_logits[0][0].argmax().item()
       A_						=      [
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""				)][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(UpperCAmelCase__				)				)
       model.save_pretrained(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      ["""[MASK]""", """[PAD]""", """[UNK]"""]

       A_						=      [json.loads(UpperCAmelCase__				) for line in open(UpperCAmelCase__				)]

       A_						=      {}
       for entry in data:
              A_						=      entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            A_						=      entity_id
                            break
                     A_						=      F'''{language}:{entity_name}'''
                     A_						=      entity_id
       return new_mapping


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
      parser.add_argument(
          '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
      )
      parser.add_argument(
          '''--entity_vocab_path''',
          default=None,
          type=str,
          help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
      )
      parser.add_argument(
          '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )
