
'''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]							)							)

'''simple docstring'''






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


UpperCAmelCase_       :   Optional[Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Any   =       list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCAmelCase_       :   str   =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		field(
        default=A						,	metadata={'help': 'Model type selected in the list: ' + ', '.join(A )} )
    lowerCAmelCase_				    =		field(
        default=A						,	metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
    lowerCAmelCase_				    =		field(
        default=128						,	metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }						,	)
    lowerCAmelCase_				    =		field(
        default=128						,	metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}						,	)
    lowerCAmelCase_				    =		field(
        default=64						,	metadata={
            'help': (
                'The maximum number of tokens for the question. Questions longer than this will '
                'be truncated to this length.'
            )
        }						,	)
    lowerCAmelCase_				    =		field(
        default=30						,	metadata={
            'help': (
                'The maximum length of an answer that can be generated. This is needed because the start '
                'and end predictions are not conditioned on one another.'
            )
        }						,	)
    lowerCAmelCase_				    =		field(
        default=A						,	metadata={'help': 'Overwrite the cached training and evaluation sets'} )
    lowerCAmelCase_				    =		field(
        default=A						,	metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
    lowerCAmelCase_				    =		field(
        default=0.0						,	metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
    lowerCAmelCase_				    =		field(
        default=20						,	metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
    lowerCAmelCase_				    =		field(
        default=0						,	metadata={
            'help': (
                'language id of input for language-specific xlm models (see'
                ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
            )
        }						,	)
    lowerCAmelCase_				    =		field(default=1						,	metadata={'help': 'multiple threads for converting example to features'} )


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'train'
    lowerCAmelCase_				    =		'dev'


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42



    def __init__(	self      :       Optional[int],__A      :       SquadDataTrainingArguments,__A      :       PreTrainedTokenizer,__A      :       Optional[int] = None,__A      :       Union[str, Split] = Split.train,__A      :       Optional[bool] = False,__A      :       Optional[str] = None,__A      :       Optional[str] = "pt",):
          _lowerCamelCase				:   Tuple								=   args
          _lowerCamelCase				:   List[str]								=   is_language_sensitive
          _lowerCamelCase				:   Dict								=   SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
          if isinstance(__A,__A     ):
                try:
                      _lowerCamelCase				:   Union[str, Any]								=   Split[mode]
                except KeyError:
                      raise KeyError("mode is not a valid split name"     )
          _lowerCamelCase				:   str								=   mode
          # Load data features from cache or dataset file
          _lowerCamelCase				:   str								=   "v2" if args.version_2_with_negative else "v1"
          _lowerCamelCase				:   Optional[Any]								=   os.path.join(
              cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          _lowerCamelCase				:   Tuple								=   cached_features_file + ".lock"
          with FileLock(__A     ):
                if os.path.exists(__A     ) and not args.overwrite_cache:
                      _lowerCamelCase				:   int								=   time.time()
                      _lowerCamelCase				:   int								=   torch.load(__A     )

                      # Legacy cache files have only features, while new cache files
                      # will have dataset and examples also.
                      _lowerCamelCase				:   Union[str, Any]								=   self.old_features["features"]
                      _lowerCamelCase				:   List[Any]								=   self.old_features.get("dataset",__A     )
                      _lowerCamelCase				:   List[Any]								=   self.old_features.get("examples",__A     )
                      logger.info(
                          f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start     )

                      if self.dataset is None or self.examples is None:
                            logger.warning(
                                f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
                                " future run"     )
                else:
                      if mode == Split.dev:
                            _lowerCamelCase				:   Dict								=   self.processor.get_dev_examples(args.data_dir     )
                      else:
                            _lowerCamelCase				:   Dict								=   self.processor.get_train_examples(args.data_dir     )

                      _lowerCamelCase						,		_lowerCamelCase				:   Dict								=   squad_convert_examples_to_features(
                          examples=self.examples,tokenizer=__A,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__A,)

                      _lowerCamelCase				:   List[Any]								=   time.time()
                      torch.save(
                          {"features": self.features, "dataset": self.dataset, "examples": self.examples},__A,)
                      # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                      logger.info(
                          f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'     )



    def __len__(	self      :       Optional[Any]     ):
          return len(self.features     )



    def __getitem__(	self      :       Tuple,__A      :       str     ):
          # Convert to Tensors and build dataset
          _lowerCamelCase				:   List[str]								=   self.features[i]

          _lowerCamelCase				:   List[str]								=   torch.tensor(feature.input_ids,dtype=torch.long     )
          _lowerCamelCase				:   Optional[int]								=   torch.tensor(feature.attention_mask,dtype=torch.long     )
          _lowerCamelCase				:   Union[str, Any]								=   torch.tensor(feature.token_type_ids,dtype=torch.long     )
          _lowerCamelCase				:   str								=   torch.tensor(feature.cls_index,dtype=torch.long     )
          _lowerCamelCase				:   str								=   torch.tensor(feature.p_mask,dtype=torch.float     )
          _lowerCamelCase				:   Tuple								=   torch.tensor(feature.is_impossible,dtype=torch.float     )

          _lowerCamelCase				:   Optional[Any]								=   {
              "input_ids": input_ids,
              "attention_mask": attention_mask,
              "token_type_ids": token_type_ids,
          }

          if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                del inputs["token_type_ids"]

          if self.args.model_type in ["xlnet", "xlm"]:
                inputs.update({"cls_index": cls_index, "p_mask": p_mask}     )
                if self.args.version_2_with_negative:
                      inputs.update({"is_impossible": is_impossible}     )
                if self.is_language_sensitive:
                      inputs.update({"langs": (torch.ones(input_ids.shape,dtype=torch.intaa     ) * self.args.lang_id)}     )

          if self.mode == Split.train:
                _lowerCamelCase				:   List[str]								=   torch.tensor(feature.start_position,dtype=torch.long     )
                _lowerCamelCase				:   Any								=   torch.tensor(feature.end_position,dtype=torch.long     )
                inputs.update({"start_positions": start_positions, "end_positions": end_positions}     )

          return inputs