



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__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Dict					):






 '''simple docstring'''

 if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
  # expert layer
  a						=		flax_key_tuple[:-1] + ("weight",)
  a						=		torch.permute(UpperCAmelCase__ ,					(0, 2, 1)					)
 elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase__					):
  # linear layer
  a						=		flax_key_tuple[:-1] + ("weight",)
  a						=		flax_tensor.T
 elif flax_key_tuple[-1] in ["scale", "embedding"]:
  a						=		flax_key_tuple[:-1] + ("weight",)

 return flax_key_tuple, flax_tensor


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 if "metadata" in layer:
  a						=		layer.split("metadata"					)
  a						=		"".join(split_layer[0]					)[:-1]
  a						=		[tuple(("metadata" + split_layer[1]).split("/"					)					)]
 elif "kvstore" in layer:
  a						=		layer.split("kvstore"					)
  a						=		"".join(split_layer[0]					)[:-1]
  a						=		[tuple(("kvstore" + split_layer[1]).split("/"					)					)]

 else:
  a						=		layer.split("/"					)
  a						=		"/".join(split_layer[:-1]					)
  a						=		(split_layer[-1],)

 if "kvstore/path" in layer:
  a						=		F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
 elif "kvstore/driver" in layer:
  a						=		"file"
 else:
  a						=		checkpoint_info[layer]

 return curr_real_layer_name, split_layer, content


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 a						=		rename_keys(UpperCAmelCase__					)
 a						=		{}
 for k, v in current_block.items():
  a						=		v
 a						=		new_current_block
 torch.save(UpperCAmelCase__ ,					UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Union[str, Any] ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Any ,					UpperCAmelCase__ :str = WEIGHTS_NAME					):






 '''simple docstring'''

 a						=		convert_file_size_to_int(UpperCAmelCase__					)
 a						=		[]
 a						=		{}
 a						=		0
 a						=		0

 os.makedirs(UpperCAmelCase__ ,					exist_ok=UpperCAmelCase__					)
 with gfile.GFile(switch_checkpoint_path + "/checkpoint" ,					"rb"					) as fp:
  a						=		serialization.msgpack_restore(fp.read()					)["optimizer"]["target"]
  a						=		flatten_dict(UpperCAmelCase__ ,					sep="/"					)

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

 for key in all_layers.keys():
  # open tensorstore file
  a						=		ts.open(unflatten_dict(all_layers[key]					)					).result().read().result()
  a						=		torch.tensor(UpperCAmelCase__					)
  a						=		raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

  # use the renaming pattern from the small conversion scripts
  a		,  a						=		rename_base_flax_keys(tuple(key.split("/"					)					) ,					UpperCAmelCase__					)
  a						=		"/".join(UpperCAmelCase__					)

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

  a						=		raw_weights.to(getattr(UpperCAmelCase__ ,					UpperCAmelCase__					)					)
  current_block_size += weight_size
  total_size += weight_size

 # Add the last block
 a						=		os.path.join(UpperCAmelCase__ ,					weights_name.replace(".bin" ,					F"""-{len(UpperCAmelCase__					)+1:05d}-of-???.bin"""					)					)
 rename_and_save_block(UpperCAmelCase__ ,					UpperCAmelCase__					)
 sharded_state_dicts.append(current_block.keys()					)

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

 # Otherwise, let's build the index
 a						=		{}
 a						=		{}
 for idx, shard in enumerate(UpperCAmelCase__					):
  a						=		weights_name.replace(
      ".bin" ,					F"""-{idx+1:05d}-of-{len(UpperCAmelCase__					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
  a						=		os.path.join(UpperCAmelCase__ ,					weights_name.replace(".bin" ,					F"""-{idx+1:05d}-of-???.bin"""					)					)
  os.rename(UpperCAmelCase__ ,					os.path.join(UpperCAmelCase__ ,					UpperCAmelCase__					)					)
  a						=		shard
  for key in shard:
   a						=		shard_file

    # Add the metadata
 a						=		{"total_size": total_size}
 a						=		{"metadata": metadata, "weight_map": weight_map}

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

 return metadata, index


if __name__ == "__main__":
     A_		:     Optional[int]        =     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_		:     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 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

 a						=		SwitchTransformersConfig.from_pretrained("google/switch-base-8"					)
 config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"					)
 a						=		SwitchTransformersForConditionalGeneration.from_pretrained(
     "/home/arthur_huggingface_co/transformers/switch_converted" ,					device_map="auto"					)

 a						=		TaTokenizer.from_pretrained("t5-small"					)
 a						=		"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

 a						=		tokenizer(UpperCAmelCase__ ,					return_tensors="pt"					).input_ids
 a						=		model.generate(UpperCAmelCase__ ,					decoder_start_token_id=0					)
 print(tokenizer.decode(out[0]					)					)






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

