




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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Tuple  ):


      '''simple docstring'''



      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            lowerCamelCase_	  =					flax_key_tuple[:-1] + ('weight',)
            lowerCamelCase_	  =					torch.permute(lowercase			,							(0, 2, 1)  )
      elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase  ):
            # linear layer
            lowerCamelCase_	  =					flax_key_tuple[:-1] + ('weight',)
            lowerCamelCase_	  =					flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            lowerCamelCase_	  =					flax_key_tuple[:-1] + ('weight',)

      return flax_key_tuple, flax_tensor

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Any			,							lowercase			: Tuple  ):


      '''simple docstring'''



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

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

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

      return curr_real_layer_name, split_layer, content

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Optional[int]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: List[str]			,							lowercase			: Optional[Any]			,							lowercase			: int			,							lowercase			: str = WEIGHTS_NAME  ):


      '''simple docstring'''



      lowerCamelCase_	  =					convert_file_size_to_int(lowercase  )
      lowerCamelCase_	  =					[]
      lowerCamelCase_	  =					{}
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0

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

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

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

            # use the renaming pattern from the small conversion scripts
            lowerCamelCase_  ,       lowerCamelCase_	  =					rename_base_flax_keys(tuple(key.split('/'  )  )			,							lowercase  )
            lowerCamelCase_	  =					'/'.join(lowercase  )

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

            lowerCamelCase_	  =					raw_weights.to(getattr(lowercase			,							lowercase  )  )
            current_block_size += weight_size
            total_size += weight_size

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

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

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

    # Add the metadata
      lowerCamelCase_	  =					{'total_size': total_size}
      lowerCamelCase_	  =					{'metadata': metadata, 'weight_map': weight_map}

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

      return metadata, index


if __name__ == "__main__":
 lowerCamelCase       :	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.",
 )
 lowerCamelCase       :	Optional[int]									=     parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

      lowerCamelCase_	  =					tokenizer(lowercase			,							return_tensors='pt'  ).input_ids
      lowerCamelCase_	  =					model.generate(lowercase			,							decoder_start_token_id=0  )
      print(tokenizer.decode(out[0]  )  )









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



