"""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 					_lowerCamelCase(					a ,  a							):
       if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
              # expert layer
              __a     				=  flax_key_tuple[:-1] + ("weight",)
              __a     				=  torch.permute(a ,  (0, 2, 1)							)
       elif flax_key_tuple[-1] == "kernel" and ".".join(a							):
              # 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 					_lowerCamelCase(					a ,  a ,  a							):
       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 					_lowerCamelCase(					a ,  a							):
       __a     				=  rename_keys(a							)
       __a     				=  {}
       for k, v in current_block.items():
              __a     				=  v
       __a     				=  new_current_block
       torch.save(a ,  a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a = WEIGHTS_NAME							):
       __a     				=  convert_file_size_to_int(a							)
       __a     				=  []
       __a     				=  {}
       __a     				=  0
       __a     				=  0

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

       __a     				=  {}
       for layer in checkpoint_info.keys():
              __a				, __a				, __a     				=  get_key_and_tensorstore_dict(
                  a ,  a ,  a							)
              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(a							)
              __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("/"							)							) ,  a							)
              __a     				=  "/".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:
                     __a     				=  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
                     __a     				=  {}
                     __a     				=  0

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

       # Add the last block
       __a     				=  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
       __a     				=  {}
       __a     				=  {}
       for idx, shard in enumerate(a							):
              __a     				=  weights_name.replace(
                  ".bin" ,  F"-{idx+1:05d}-of-{len(a							):05d}.bin"							)  # len(sharded_state_dicts):05d}
              __a     				=  os.path.join(a ,  weights_name.replace(".bin" ,  F"-{idx+1:05d}-of-???.bin"							)							)
              os.rename(a ,  os.path.join(a ,  a							)							)
              __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(a ,  a							) ,  "w" ,  encoding="utf-8"							) as f:
              __a     				=  json.dumps(a ,  indent=2 ,  sort_keys=a							) + "\n"
              f.write(a							)

       return metadata, index


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:str			=	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.""",
   )
   SCREAMING_SNAKE_CASE__: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 					_lowerCamelCase(					):
       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(a ,  return_tensors="pt"							).input_ids
       __a     				=  model.generate(a ,  decoder_start_token_id=0							)
       print(tokenizer.decode(out[0]							)							)

"""simple docstring"""



from ...processing_utils import ProcessorMixin






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[str]           =		"""WhisperFeatureExtractor"""
 _snake_case   :			Any           =		"""WhisperTokenizer"""


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)
        __a     				=  self.feature_extractor
        __a     				=  False


 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=True		):
        return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase  ,						language=lowerCamelCase  ,						no_timestamps=lowerCamelCase		)


 def __call__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*lowerCamelCase  ,						**lowerCamelCase		)

        __a     				=  kwargs.pop("audio"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("sampling_rate"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("text"  ,						lowerCamelCase		)
        if len(lowerCamelCase		) > 0:
               __a     				=  args[0]
               __a     				=  args[1:]

        if audio is None and text is None:
               raise ValueError("You need to specify either an `audio` or `text` input to process."		)

        if audio is not None:
               __a     				=  self.feature_extractor(lowerCamelCase  ,						*lowerCamelCase  ,						sampling_rate=lowerCamelCase  ,						**lowerCamelCase		)
        if text is not None:
               __a     				=  self.tokenizer(lowerCamelCase  ,						**lowerCamelCase		)

        if text is None:
               return inputs

        elif audio is None:
               return encodings
        else:
               __a     				=  encodings["input_ids"]
               return inputs


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase="np"		):
        return self.tokenizer.get_prompt_ids(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
