'''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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          lowercase_					:       int     				=					flax_key_tuple[:-1] + ('weight',)
          lowercase_					:       int     				=					torch.permute(lowerCAmelCase__  ,       (0, 2, 1)							)
     elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__							):
          # linear layer
          lowercase_					:       Optional[int]     				=					flax_key_tuple[:-1] + ('weight',)
          lowercase_					:       Optional[Any]     				=					flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          lowercase_					:       Union[str, Any]     				=					flax_key_tuple[:-1] + ('weight',)

     return flax_key_tuple, flax_tensor

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if "metadata" in layer:
          lowercase_					:       str     				=					layer.split('metadata'							)
          lowercase_					:       int     				=					''.join(split_layer[0]							)[:-1]
          lowercase_					:       Dict     				=					[tuple(('metadata' + split_layer[1]).split('/'							)							)]
     elif "kvstore" in layer:
          lowercase_					:       int     				=					layer.split('kvstore'							)
          lowercase_					:       Dict     				=					''.join(split_layer[0]							)[:-1]
          lowercase_					:       List[str]     				=					[tuple(('kvstore' + split_layer[1]).split('/'							)							)]

     else:
          lowercase_					:       Any     				=					layer.split('/'							)
          lowercase_					:       List[Any]     				=					'/'.join(split_layer[:-1]							)
          lowercase_					:       Optional[int]     				=					(split_layer[-1],)

     if "kvstore/path" in layer:
          lowercase_					:       Optional[Any]     				=					f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
     elif "kvstore/driver" in layer:
          lowercase_					:       int     				=					'file'
     else:
          lowercase_					:       int     				=					checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					rename_keys(lowerCAmelCase__							)
     lowercase_					:       Optional[int]     				=					{}
     for k, v in current_block.items():
          lowercase_					:       Optional[Any]     				=					v
     lowercase_					:       Optional[int]     				=					new_current_block
     torch.save(lowerCAmelCase__  ,       lowerCAmelCase__							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME							):
     lowercase_					:       Tuple     				=					convert_file_size_to_int(lowerCAmelCase__							)
     lowercase_					:       List[Any]     				=					[]
     lowercase_					:       Optional[Any]     				=					{}
     lowercase_					:       int     				=					0
     lowercase_					:       int     				=					0

     os.makedirs(lowerCAmelCase__  ,       exist_ok=lowerCAmelCase__							)
     with gfile.GFile(switch_checkpoint_path + '/checkpoint'  ,       'rb'							) as fp:
          lowercase_					:       str     				=					serialization.msgpack_restore(fp.read()							)['optimizer']['target']
          lowercase_					:       Optional[Any]     				=					flatten_dict(lowerCAmelCase__  ,       sep='/'							)

     lowercase_					:       Union[str, Any]     				=					{}
     for layer in checkpoint_info.keys():
          lowercase_				,lowercase_				,lowercase_					:       Optional[int]     				=					get_key_and_tensorstore_dict(
              lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__							)
          if curr_real_layer_name in all_layers:
               lowercase_					:       List[Any]     				=					content
          else:
               lowercase_					:       Dict     				=					{split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          lowercase_				,lowercase_					:       Any     				=					rename_base_flax_keys(tuple(key.split('/'							)							)  ,       lowerCAmelCase__							)
          lowercase_					:       List[str]     				=					'/'.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:
               lowercase_					:       List[Any]     				=					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
               lowercase_					:       int     				=					{}
               lowercase_					:       Dict     				=					0

          lowercase_					:       str     				=					raw_weights.to(getattr(lowerCAmelCase__  ,       lowerCAmelCase__							)							)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     lowercase_					:       Optional[int]     				=					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
     lowercase_					:       Dict     				=					{}
     lowercase_					:       List[str]     				=					{}
     for idx, shard in enumerate(lowerCAmelCase__							):
          lowercase_					:       Optional[Any]     				=					weights_name.replace(
              '.bin'  ,       f'''-{idx+1:05d}-of-{len(lowerCAmelCase__							):05d}.bin'''							)  # len(sharded_state_dicts):05d}
          lowercase_					:       Optional[Any]     				=					os.path.join(lowerCAmelCase__  ,       weights_name.replace('.bin'  ,       f'''-{idx+1:05d}-of-???.bin'''							)							)
          os.rename(lowerCAmelCase__  ,       os.path.join(lowerCAmelCase__  ,       lowerCAmelCase__							)							)
          lowercase_					:       Optional[int]     				=					shard
          for key in shard:
               lowercase_					:       List[str]     				=					shard_file

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

     with open(os.path.join(lowerCAmelCase__  ,       lowerCAmelCase__							)  ,       'w'  ,       encoding='utf-8'							) as f:
          lowercase_					:       List[Any]     				=					json.dumps(lowerCAmelCase__  ,       indent=2  ,       sort_keys=lowerCAmelCase__							) + '\n'
          f.write(lowerCAmelCase__							)

     return metadata, index


if __name__ == "__main__":
 _A								=   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.',
 )
 _A								=   parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )






def _UpperCamelCase   (       ):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     lowercase_					:       str     				=					SwitchTransformersConfig.from_pretrained('google/switch-base-8'							)
     config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'							)
     lowercase_					:       str     				=					SwitchTransformersForConditionalGeneration.from_pretrained(
         '/home/arthur_huggingface_co/transformers/switch_converted'  ,       device_map='auto'							)

     lowercase_					:       Optional[int]     				=					TaTokenizer.from_pretrained('t5-small'							)
     lowercase_					:       Tuple     				=					'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

     lowercase_					:       Optional[int]     				=					tokenizer(lowerCAmelCase__  ,       return_tensors='pt'							).input_ids
     lowercase_					:       str     				=					model.generate(lowerCAmelCase__  ,       decoder_start_token_id=0							)
     print(tokenizer.decode(out[0]							)							)





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




