
'''simple docstring'''






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
 raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
 raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Dict   =       'Hello, World!'
UpperCAmelCase_       :   Tuple   =       'en_XX'







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



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   Path("data_bin"							)
      _lowerCamelCase				:   Union[str, Any]								=   FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(_lowerCAmelCase							).parent							)				,	checkpoint_file=Path(_lowerCAmelCase							).name				,	_name="xmod_base"				,	arch="xmod_base"				,	task="multilingual_masked_lm"				,	data_name_or_path=str(_lowerCAmelCase							)				,	bpe="sentencepiece"				,	sentencepiece_model=str(Path(_lowerCAmelCase							).parent / "sentencepiece.bpe.model"							)				,	src_dict=str(data_dir / "dict.txt"							)				,	)
      xmod.eval()  # disable dropout
      print(_lowerCAmelCase							)

      _lowerCamelCase				:   Tuple								=   xmod.model.encoder.sentence_encoder
      _lowerCamelCase				:   List[str]								=   XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,	hidden_size=xmod.cfg.model.encoder_embed_dim				,	num_hidden_layers=xmod.cfg.model.encoder_layers				,	num_attention_heads=xmod.cfg.model.encoder_attention_heads				,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,	max_position_embeddings=514				,	type_vocab_size=1				,	layer_norm_eps=1E-5				,	pre_norm=xmod.cfg.model.encoder_normalize_before				,	adapter_reduction_factor=getattr(xmod.cfg.model				,	"bottleneck"				,	2							)				,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,	ln_before_adapter=xmod.cfg.model.ln_before_adapter				,	languages=xmod.cfg.model.languages				,	)
      if classification_head:
            _lowerCamelCase				:   List[Any]								=   xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

      print("Our X-MOD config:"				,	_lowerCAmelCase							)

      _lowerCamelCase				:   Optional[Any]								=   XmodForSequenceClassification(_lowerCAmelCase							) if classification_head else XmodForMaskedLM(_lowerCAmelCase							)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      _lowerCamelCase				:   str								=   xmod_sent_encoder.embed_tokens.weight
      _lowerCamelCase				:   List[Any]								=   xmod_sent_encoder.embed_positions.weight
      _lowerCamelCase				:   Optional[int]								=   torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c xmod doesn't use them.

      _lowerCamelCase				:   Any								=   xmod_sent_encoder.layernorm_embedding.weight
      _lowerCamelCase				:   str								=   xmod_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers							):
            # Encoder: start of layer
            _lowerCamelCase				:   Optional[Any]								=   model.roberta.encoder.layer[i]
            _lowerCamelCase				:   str								=   xmod_sent_encoder.layers[i]

            # self attention
            _lowerCamelCase				:   Any								=   layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)							)
            ):
                  raise AssertionError("Dimensions of self-attention weights do not match."							)

            _lowerCamelCase				:   Tuple								=   xmod_layer.self_attn.q_proj.weight
            _lowerCamelCase				:   Optional[int]								=   xmod_layer.self_attn.q_proj.bias
            _lowerCamelCase				:   Tuple								=   xmod_layer.self_attn.k_proj.weight
            _lowerCamelCase				:   Optional[int]								=   xmod_layer.self_attn.k_proj.bias
            _lowerCamelCase				:   Union[str, Any]								=   xmod_layer.self_attn.v_proj.weight
            _lowerCamelCase				:   Dict								=   xmod_layer.self_attn.v_proj.bias

            # self-attention output
            _lowerCamelCase				:   Dict								=   layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError("Dimensions of self-attention output weights do not match."							)
            _lowerCamelCase				:   List[Any]								=   xmod_layer.self_attn.out_proj.weight
            _lowerCamelCase				:   Union[str, Any]								=   xmod_layer.self_attn.out_proj.bias
            _lowerCamelCase				:   str								=   xmod_layer.self_attn_layer_norm.weight
            _lowerCamelCase				:   Tuple								=   xmod_layer.self_attn_layer_norm.bias

            # intermediate
            _lowerCamelCase				:   int								=   layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of intermediate weights do not match."							)
            _lowerCamelCase				:   Any								=   xmod_layer.fca.weight
            _lowerCamelCase				:   Dict								=   xmod_layer.fca.bias

            # output
            _lowerCamelCase				:   Tuple								=   layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of feed-forward weights do not match."							)
            _lowerCamelCase				:   str								=   xmod_layer.fca.weight
            _lowerCamelCase				:   Any								=   xmod_layer.fca.bias
            _lowerCamelCase				:   List[str]								=   xmod_layer.final_layer_norm.weight
            _lowerCamelCase				:   List[Any]								=   xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  _lowerCamelCase				:   Union[str, Any]								=   xmod_layer.adapter_layer_norm.weight
                  _lowerCamelCase				:   Tuple								=   xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys()							) != sorted(xmod_layer.adapter_modules.keys()							):
                  raise AssertionError("Lists of language adapters do not match."							)
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  _lowerCamelCase				:   Optional[int]								=   bert_output.adapter_modules[lang_code]
                  _lowerCamelCase				:   List[Any]								=   xmod_layer.adapter_modules[lang_code]
                  _lowerCamelCase				:   int								=   from_adapter.fca.weight
                  _lowerCamelCase				:   Dict								=   from_adapter.fca.bias
                  _lowerCamelCase				:   int								=   from_adapter.fca.weight
                  _lowerCamelCase				:   Any								=   from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            _lowerCamelCase				:   str								=   xmod_sent_encoder.layer_norm.weight
            _lowerCamelCase				:   Optional[Any]								=   xmod_sent_encoder.layer_norm.bias

      if classification_head:
            _lowerCamelCase				:   int								=   xmod.model.classification_heads["mnli"].dense.weight
            _lowerCamelCase				:   Any								=   xmod.model.classification_heads["mnli"].dense.bias
            _lowerCamelCase				:   List[Any]								=   xmod.model.classification_heads["mnli"].out_proj.weight
            _lowerCamelCase				:   Optional[Any]								=   xmod.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            _lowerCamelCase				:   Any								=   xmod.model.encoder.lm_head.dense.weight
            _lowerCamelCase				:   Any								=   xmod.model.encoder.lm_head.dense.bias
            _lowerCamelCase				:   Optional[int]								=   xmod.model.encoder.lm_head.layer_norm.weight
            _lowerCamelCase				:   Optional[int]								=   xmod.model.encoder.lm_head.layer_norm.bias
            _lowerCamelCase				:   Optional[int]								=   xmod.model.encoder.lm_head.weight
            _lowerCamelCase				:   List[Any]								=   xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      _lowerCamelCase				:   Any								=   xmod.encode(_lowerCAmelCase							).unsqueeze(0							)  # batch of size 1
      model.roberta.set_default_language(_lowerCAmelCase							)

      _lowerCamelCase				:   int								=   model(_lowerCAmelCase							)[0]
      if classification_head:
            _lowerCamelCase				:   List[str]								=   xmod.model.classification_heads["mnli"](xmod.extract_features(_lowerCAmelCase							)							)
      else:
            _lowerCamelCase				:   str								=   xmod.model(_lowerCAmelCase				,	lang_id=[SAMPLE_LANGUAGE]							)[0]
      print(our_output.shape				,	their_output.shape							)
      _lowerCamelCase				:   Dict								=   torch.max(torch.abs(our_output - their_output							)							).item()
      print(F'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
      _lowerCamelCase				:   Dict								=   torch.allclose(_lowerCAmelCase				,	_lowerCAmelCase				,	atol=1E-3							)
      print("Do both models output the same tensors?"				,	"🔥" if success else "💩"							)
      if not success:
            raise Exception("Something went wRoNg"							)

      Path(_lowerCAmelCase							).mkdir(parents=_lowerCAmelCase				,	exist_ok=_lowerCAmelCase							)
      print(F'Saving model to {pytorch_dump_folder_path}'							)
      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   Optional[Any]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--classification_head', action='store_true', help='Whether to convert a final classification head.'
 )
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )

'''simple docstring'''






import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            _lowerCamelCase				:   List[str]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   Union[str, Any]								=   torch.permute(_lowerCAmelCase				,	(0, 2, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase							):
            # linear layer
            _lowerCamelCase				:   Union[str, Any]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   int								=   flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCamelCase				:   Tuple								=   flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor







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



      """simple docstring"""




      if "metadata" in layer:
            _lowerCamelCase				:   Optional[int]								=   layer.split("metadata"							)
            _lowerCamelCase				:   Union[str, Any]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   Dict								=   [tuple(("metadata" + split_layer[1]).split("/"							)							)]
      elif "kvstore" in layer:
            _lowerCamelCase				:   List[str]								=   layer.split("kvstore"							)
            _lowerCamelCase				:   Optional[int]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   List[Any]								=   [tuple(("kvstore" + split_layer[1]).split("/"							)							)]

      else:
            _lowerCamelCase				:   Tuple								=   layer.split("/"							)
            _lowerCamelCase				:   int								=   "/".join(split_layer[:-1]							)
            _lowerCamelCase				:   Optional[Any]								=   (split_layer[-1],)

      if "kvstore/path" in layer:
            _lowerCamelCase				:   int								=   F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
      elif "kvstore/driver" in layer:
            _lowerCamelCase				:   Optional[int]								=   "file"
      else:
            _lowerCamelCase				:   str								=   checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content







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



      """simple docstring"""




      _lowerCamelCase				:   str								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {}
      for k, v in current_block.items():
            _lowerCamelCase				:   Union[str, Any]								=   v
      _lowerCamelCase				:   str								=   new_current_block
      torch.save(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    str = WEIGHTS_NAME							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   convert_file_size_to_int(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   []
      _lowerCamelCase				:   Dict								=   {}
      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   List[str]								=   0

      os.makedirs(_lowerCAmelCase				,	exist_ok=_lowerCAmelCase							)
      with gfile.GFile(switch_checkpoint_path + "/checkpoint"				,	"rb"							) as fp:
            _lowerCamelCase				:   Tuple								=   serialization.msgpack_restore(fp.read()							)["optimizer"]["target"]
            _lowerCamelCase				:   Union[str, Any]								=   flatten_dict(_lowerCAmelCase				,	sep="/"							)

      _lowerCamelCase				:   Optional[int]								=   {}
      for layer in checkpoint_info.keys():
            _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   int								=   get_key_and_tensorstore_dict(
                _lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            if curr_real_layer_name in all_layers:
                  _lowerCamelCase				:   Optional[int]								=   content
            else:
                  _lowerCamelCase				:   int								=   {split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            _lowerCamelCase				:   Any								=   ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
            _lowerCamelCase				:   int								=   torch.tensor(_lowerCAmelCase							)
            _lowerCamelCase				:   str								=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

            # use the renaming pattern from the small conversion scripts
            _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   rename_base_flax_keys(tuple(key.split("/"							)							)				,	_lowerCAmelCase							)
            _lowerCamelCase				:   Optional[Any]								=   "/".join(_lowerCAmelCase							)

            # If this weight is going to tip up over the maximal size, we split.
            if current_block_size + weight_size > max_shard_size:
                  _lowerCamelCase				:   Tuple								=   os.path.join(
                      _lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{len(_lowerCAmelCase							)+1:05d}-of-???.bin'							)							)
                  rename_and_save_block(_lowerCAmelCase				,	_lowerCAmelCase							)
                  sharded_state_dicts.append(current_block.keys()							)
                  del current_block
                  _lowerCamelCase				:   Optional[int]								=   {}
                  _lowerCamelCase				:   Dict								=   0

            _lowerCamelCase				:   Optional[int]								=   raw_weights.to(getattr(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      _lowerCamelCase				:   List[str]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{len(_lowerCAmelCase							)+1:05d}-of-???.bin'							)							)
      rename_and_save_block(_lowerCAmelCase				,	_lowerCAmelCase							)
      sharded_state_dicts.append(current_block.keys()							)

      # If we only have one shard, we return it
      if len(_lowerCAmelCase							) == 1:
            return {weights_name: sharded_state_dicts[0]}, None

      # Otherwise, let's build the index
      _lowerCamelCase				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   str								=   {}
      for idx, shard in enumerate(_lowerCAmelCase							):
            _lowerCamelCase				:   Dict								=   weights_name.replace(
                ".bin"				,	F'-{idx+1:05d}-of-{len(_lowerCAmelCase							):05d}.bin'							)  # len(sharded_state_dicts):05d}
            _lowerCamelCase				:   Union[str, Any]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{idx+1:05d}-of-???.bin'							)							)
            os.rename(_lowerCAmelCase				,	os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Tuple								=   shard
            for key in shard:
                  _lowerCamelCase				:   str								=   shard_file

    # Add the metadata
      _lowerCamelCase				:   Optional[Any]								=   {"total_size": total_size}
      _lowerCamelCase				:   Dict								=   {"metadata": metadata, "weight_map": weight_map}

      with open(os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)				,	"w"				,	encoding="utf-8"							) as f:
            _lowerCamelCase				:   int								=   json.dumps(_lowerCAmelCase				,	indent=2				,	sort_keys=_lowerCAmelCase							) + "\n"
            f.write(_lowerCAmelCase							)

      return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_       :   List[Any]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--switch_t5x_checkpoint_path',
     default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
     type=str,
     required=False,
     help='Path to a directory containing a folder per layer. Follows the original Google format.',
 )
 parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
 parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
     type=str,
     required=False,
     help='Path to the output pytorch model.',
 )
 UpperCAmelCase_       :   Dict   =       parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )







def 		A_       (	):



      """simple docstring"""




      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      _lowerCamelCase				:   Optional[int]								=   SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
      config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
      _lowerCamelCase				:   int								=   SwitchTransformersForConditionalGeneration.from_pretrained(
          "/home/arthur_huggingface_co/transformers/switch_converted"				,	device_map="auto"							)

      _lowerCamelCase				:   List[str]								=   TaTokenizer.from_pretrained("t5-small"							)
      _lowerCamelCase				:   Tuple								=   "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

      _lowerCamelCase				:   Optional[int]								=   tokenizer(_lowerCAmelCase				,	return_tensors="pt"							).input_ids
      _lowerCamelCase				:   List[Any]								=   model.generate(_lowerCAmelCase				,	decoder_start_token_id=0							)
      print(tokenizer.decode(out[0]							)							)