


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    lowercase_	(	_A    :					Optional[int]					,      _A    :					List[str]		)     ->   str:

      """simple docstring"""




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

      return flax_key_tuple, flax_tensor

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Optional[Any]					,      _A    :					List[str]		)     ->   int:

      """simple docstring"""




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

      else:
            lowerCamelCase__	:       Any					=						layer.split("/"		)
            lowerCamelCase__	:       Dict					=						"""/""".join(split_layer[:-1]		)
            lowerCamelCase__	:       List[str]					=						(split_layer[-1],)

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

      return curr_real_layer_name, split_layer, content

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Union[str, Any]		)     ->   List[Any]:

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						rename_keys(__A		)
      lowerCamelCase__	:       Optional[Any]					=						{}
      for k, v in current_block.items():
            lowerCamelCase__	:       Optional[Any]					=						v
      lowerCamelCase__	:       Dict					=						new_current_block
      torch.save(__A					,      __A		)

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Any					,      _A    :					Any					,      _A    :					str					,      _A    :					int = WEIGHTS_NAME		)     ->   Any:

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						convert_file_size_to_int(__A		)
      lowerCamelCase__	:       List[str]					=						[]
      lowerCamelCase__	:       List[str]					=						{}
      lowerCamelCase__	:       Any					=						0
      lowerCamelCase__	:       Tuple					=						0

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

      lowerCamelCase__	:       int					=						{}
      for layer in checkpoint_info.keys():
            lowerCamelCase__	:       Union[str, Any]					=						get_key_and_tensorstore_dict(
                __A					,      __A					,      __A		)
            if curr_real_layer_name in all_layers:
                  lowerCamelCase__	:       Tuple					=						content
            else:
                  lowerCamelCase__	:       Tuple					=						{split_layer[-1]: content}

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

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

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

            lowerCamelCase__	:       List[Any]					=						raw_weights.to(getattr(__A					,      __A		)		)
            current_block_size += weight_size
            total_size += weight_size

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

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

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

    # Add the metadata
      lowerCamelCase__	:       List[str]					=						{"""total_size""": total_size}
      lowerCamelCase__	:       Optional[Any]					=						{"""metadata""": metadata, """weight_map""": weight_map}

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

      return metadata, index


if __name__ == "__main__":
    A   :					Dict	=      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   :					str	=      parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )


def    lowercase_	(	)     ->   List[str]:

      """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__	:       Union[str, Any]					=						SwitchTransformersForConditionalGeneration.from_pretrained(
          "/home/arthur_huggingface_co/transformers/switch_converted"					,      device_map="auto"		)

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

      lowerCamelCase__	:       Tuple					=						tokenizer(__A					,      return_tensors="pt"		).input_ids
      lowerCamelCase__	:       str					=						model.generate(__A					,      decoder_start_token_id=0		)
      print(tokenizer.decode(out[0]		)		)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,   dtype=tf.intaa      ,   )  # J'aime le camembert !"

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   dtype=tf.floataa      ,   )
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,   expected_slice.numpy()      ,   atol=1E-4 ) )


