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







def 			__UpperCamelCase     (    A			,				A			,				A			,				A			,				A = WEIGHTS_NAME				):
     UpperCamelCase__					    =   convert_file_size_to_int(SCREAMING_SNAKE_CASE_				)
     UpperCamelCase__					    =   []
     UpperCamelCase__					    =   {}
     UpperCamelCase__					    =   0
     UpperCamelCase__					    =   0

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

     UpperCamelCase__					    =   {}
     for layer in checkpoint_info.keys():
          UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__					    =   get_key_and_tensorstore_dict(
              SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)
          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(SCREAMING_SNAKE_CASE_				)
          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('''/'''				)				)			,				SCREAMING_SNAKE_CASE_				)
          UpperCamelCase__					    =   '''/'''.join(SCREAMING_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:
               UpperCamelCase__					    =   os.path.join(
                   SCREAMING_SNAKE_CASE_			,				weights_name.replace('''.bin'''			,				f"-{len(SCREAMING_SNAKE_CASE_				)+1:05d}-of-???.bin"				)				)
               rename_and_save_block(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)
               sharded_state_dicts.append(current_block.keys()				)
               del current_block
               UpperCamelCase__					    =   {}
               UpperCamelCase__					    =   0

          UpperCamelCase__					    =   raw_weights.to(getattr(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)				)
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     UpperCamelCase__					    =   {}
     UpperCamelCase__					    =   {}
     for idx, shard in enumerate(SCREAMING_SNAKE_CASE_				):
          UpperCamelCase__					    =   weights_name.replace(
              '''.bin'''			,				f"-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_				):05d}.bin"				)  # len(sharded_state_dicts):05d}
          UpperCamelCase__					    =   os.path.join(SCREAMING_SNAKE_CASE_			,				weights_name.replace('''.bin'''			,				f"-{idx+1:05d}-of-???.bin"				)				)
          os.rename(SCREAMING_SNAKE_CASE_			,				os.path.join(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)				)
          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(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)			,				'''w'''			,				encoding='''utf-8'''				) as f:
          UpperCamelCase__					    =   json.dumps(SCREAMING_SNAKE_CASE_			,				indent=2			,				sort_keys=SCREAMING_SNAKE_CASE_				) + '''\n'''
          f.write(SCREAMING_SNAKE_CASE_				)

     return metadata, index


if __name__ == "__main__":
     __magic_name__    		=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__    		=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(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''				).input_ids
     UpperCamelCase__					    =   model.generate(SCREAMING_SNAKE_CASE_			,				decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)









'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

			# since string of zero length match pattern of zero length
			A_									= 1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= dp[0][j - 2] if pattern[j - 1] == '*' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															A_									= 1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															A_									= dp[i - 1][j]
												else:
															A_									= 0
									else:
												A_									= 0

			return bool(dp[-1][-1]     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							_SCREAMING_SNAKE_CASE			=						"aab"
							_SCREAMING_SNAKE_CASE			=						"c*a*b"

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(f"""{input_string} matches the given pattern {pattern}""")
							else:
														print(f"""{input_string} does not match with the given pattern {pattern}""")




