


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				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					int				):

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

     return flax_key_tuple, flax_tensor

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Tuple				):

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

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

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

     return curr_real_layer_name, split_layer, content

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					Optional[int]				):
     __UpperCamelCase    							=rename_keys(__a				)
     __UpperCamelCase    							={}
     for k, v in current_block.items():
          __UpperCamelCase    							=v
     __UpperCamelCase    							=new_current_block
     torch.save(__a			,     __a				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					str = WEIGHTS_NAME				):
     __UpperCamelCase    							=convert_file_size_to_int(__a				)
     __UpperCamelCase    							=[]
     __UpperCamelCase    							={}
     __UpperCamelCase    							=0
     __UpperCamelCase    							=0

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

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

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

          # use the renaming pattern from the small conversion scripts
          __UpperCamelCase							,      __UpperCamelCase    							=rename_base_flax_keys(tuple(key.split('/'				)				)			,     __a				)
          __UpperCamelCase    							='/'.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:
               __UpperCamelCase    							=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
               __UpperCamelCase    							={}
               __UpperCamelCase    							=0

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

     # Add the last block
     __UpperCamelCase    							=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
     __UpperCamelCase    							={}
     __UpperCamelCase    							={}
     for idx, shard in enumerate(__a				):
          __UpperCamelCase    							=weights_name.replace(
              '.bin'			,     F'-{idx+1:05d}-of-{len(__a				):05d}.bin'				)  # len(sharded_state_dicts):05d}
          __UpperCamelCase    							=os.path.join(__a			,     weights_name.replace('.bin'			,     F'-{idx+1:05d}-of-???.bin'				)				)
          os.rename(__a			,     os.path.join(__a			,     __a				)				)
          __UpperCamelCase    							=shard
          for key in shard:
               __UpperCamelCase    							=shard_file

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

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

     return metadata, index


if __name__ == "__main__":
   _A		       =						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		       =						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				(			):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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












class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			:
			'''simple docstring'''




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







					"""simple docstring"""






					A__        =   [
					    [],
					    [],
					    [],
					]






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					try:
							if len(self.queues[priority]   ) >= 1_00:
									raise OverflowError("""Maximum queue size is 100"""   )
							self.queues[priority].append(__lowerCAmelCase   )
					except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""   )






			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					for queue in self.queues:
							if queue:
									return queue.pop(0   )
					raise UnderFlowError("""All queues are empty"""   )







			def __str__(			self   :    Tuple   )	->	str:







					"""simple docstring"""






					return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues   )   )


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int   )	->	str:







					"""simple docstring"""






					A__        =   []






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					if len(self.queue   ) == 1_00:
							raise OverFlowError("""Maximum queue size is 100"""   )
					self.queue.append(__lowerCAmelCase   )






			def    a_       (			self   :    List[str]   )	->	int:







					"""simple docstring"""






					if not self.queue:
							raise UnderFlowError("""The queue is empty"""   )
					else:
							A__        =   min(self.queue   )
							self.queue.remove(__lowerCAmelCase   )
							return data







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







					"""simple docstring"""






					return str(self.queue   )







def    __lowerCamelCase					(							)		->				Optional[Any]:






		"""simple docstring"""

		A__        =   FixedPriorityQueue()
		fpq.enqueue(0    ,     1_0    )
		fpq.enqueue(1    ,     7_0    )
		fpq.enqueue(0    ,     1_0_0    )
		fpq.enqueue(2    ,     1    )
		fpq.enqueue(2    ,     5    )
		fpq.enqueue(1    ,     7    )
		fpq.enqueue(2    ,     4    )
		fpq.enqueue(1    ,     6_4    )
		fpq.enqueue(0    ,     1_2_8    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )







def    __lowerCamelCase					(							)		->				int:






		"""simple docstring"""

		A__        =   ElementPriorityQueue()
		epq.enqueue(1_0    )
		epq.enqueue(7_0    )
		epq.enqueue(1_0_0    )
		epq.enqueue(1    )
		epq.enqueue(5    )
		epq.enqueue(7    )
		epq.enqueue(4    )
		epq.enqueue(6_4    )
		epq.enqueue(1_2_8    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()



