





'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  return flax_key_tuple, flax_tensor



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

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

  return curr_real_layer_name, split_layer, content



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = WEIGHTS_NAME       ):







  '''simple docstring'''



  _snake_case     		=				convert_file_size_to_int(snake_case__       )
  _snake_case     		=				[]
  _snake_case     		=				{}
  _snake_case     		=				0
  _snake_case     		=				0

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

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

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

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

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

    _snake_case     		=				raw_weights.to(getattr(snake_case__					,	snake_case__       )       )
    current_block_size += weight_size
    total_size += weight_size

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

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

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

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

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

  return metadata, index


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




def 				snake_case_      (		):







  '''simple docstring'''



  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









'''simple docstring'''



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     Adafactor,
     AdamW,
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_inverse_sqrt_schedule,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for step in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

        _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__       )
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE__       )
  return lrs


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,   [0.4, 0.2, -0.5]						,   tol=1e-2	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,   [0.4, 0.2, -0.5]						,   tol=1e-2	)







@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   nn.Linear(50					,    50			) if is_torch_available() else None
     UpperCAmelCase__       :						str      =   AdamW(m.parameters()					,    lr=10.0			) if is_torch_available() else None
     UpperCAmelCase__       :						Optional[Any]      =   10

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase						,   msg=lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {"num_warmup_steps": 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, "num_cycles": 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {"num_warmup_steps": 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
         _snake_case		,		_snake_case     		=				data

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         self.assertEqual(len([scheduler.get_lr()[0]]	)						,   1	)
         _snake_case     		=				unwrap_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListAlmostEqual(
             lowerCamelCase						,   lowerCamelCase						,   tol=1e-2						,   msg=F'''failed for {scheduler_func} in normal scheduler'''						,   )

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         if scheduler_func.__name__ != "get_constant_schedule":
           LambdaScheduleWrapper.wrap_scheduler(lowerCamelCase	)  # wrap to test picklability of the schedule
         _snake_case     		=				unwrap_and_save_reload_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListEqual(lowerCamelCase						,   lowerCamelCase						,   msg=F'''failed for {scheduler_func} in save and reload'''	)







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

     def __call__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       return self.fn(*lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


