






"""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 						lowercase__(A ,		A						)    ->str:




						"""simple docstring"""



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

						return flax_key_tuple, flax_tensor





def 						lowercase__(A ,		A ,		A						)    ->Dict:




						"""simple docstring"""



						if "metadata" in layer:
												lowercase__    :				int=	layer.split("metadata"						)
												lowercase__    :				Optional[int]=	"".join(split_layer[0]						)[:-1]
												lowercase__    :				Tuple=	[tuple(("metadata" + split_layer[1]).split("/"						)						)]
						elif "kvstore" in layer:
												lowercase__    :				Optional[int]=	layer.split("kvstore"						)
												lowercase__    :				Optional[int]=	"".join(split_layer[0]						)[:-1]
												lowercase__    :				Dict=	[tuple(("kvstore" + split_layer[1]).split("/"						)						)]

						else:
												lowercase__    :				Union[str, Any]=	layer.split("/"						)
												lowercase__    :				Union[str, Any]=	"/".join(split_layer[:-1]						)
												lowercase__    :				Tuple=	(split_layer[-1],)

						if "kvstore/path" in layer:
												lowercase__    :				Optional[int]=	f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												lowercase__    :				Optional[Any]=	"file"
						else:
												lowercase__    :				Union[str, Any]=	checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content





def 						lowercase__(A ,		A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	rename_keys(A						)
						lowercase__    :				Dict=	{}
						for k, v in current_block.items():
												lowercase__    :				Union[str, Any]=	v
						lowercase__    :				Any=	new_current_block
						torch.save(A ,		A						)





def 						lowercase__(A ,		A ,		A ,		A ,		A = WEIGHTS_NAME						)    ->int:




						"""simple docstring"""



						lowercase__    :				List[Any]=	convert_file_size_to_int(A						)
						lowercase__    :				Tuple=	[]
						lowercase__    :				Tuple=	{}
						lowercase__    :				str=	0
						lowercase__    :				List[str]=	0

						os.makedirs(A ,		exist_ok=A						)
						with gfile.GFile(switch_checkpoint_path + "/checkpoint" ,		"rb"						) as fp:
												lowercase__    :				Union[str, Any]=	serialization.msgpack_restore(fp.read()						)["optimizer"]["target"]
												lowercase__    :				Optional[int]=	flatten_dict(A ,		sep="/"						)

						lowercase__    :				List[str]=	{}
						for layer in checkpoint_info.keys():
												lowercase__,     lowercase__,     lowercase__    :				Union[str, Any]=	get_key_and_tensorstore_dict(
												    A ,		A ,		A						)
												if curr_real_layer_name in all_layers:
																		lowercase__    :				Dict=	content
												else:
																		lowercase__    :				Dict=	{split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												lowercase__    :				str=	ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
												lowercase__    :				Optional[int]=	torch.tensor(A						)
												lowercase__    :				List[str]=	raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

												# use the renaming pattern from the small conversion scripts
												lowercase__,     lowercase__    :				Union[str, Any]=	rename_base_flax_keys(tuple(key.split("/"						)						) ,		A						)
												lowercase__    :				List[Any]=	"/".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:
																		lowercase__    :				Tuple=	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
																		lowercase__    :				List[str]=	{}
																		lowercase__    :				List[str]=	0

												lowercase__    :				Optional[int]=	raw_weights.to(getattr(A ,		A						)						)
												current_block_size += weight_size
												total_size += weight_size

						# Add the last block
						lowercase__    :				Tuple=	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
						lowercase__    :				List[str]=	{}
						lowercase__    :				str=	{}
						for idx, shard in enumerate(A						):
												lowercase__    :				Optional[int]=	weights_name.replace(
												    ".bin" ,		f'''-{idx+1:05d}-of-{len(A						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
												lowercase__    :				Dict=	os.path.join(A ,		weights_name.replace(".bin" ,		f'''-{idx+1:05d}-of-???.bin'''						)						)
												os.rename(A ,		os.path.join(A ,		A						)						)
												lowercase__    :				Optional[int]=	shard
												for key in shard:
																		lowercase__    :				Union[str, Any]=	shard_file

    # Add the metadata
						lowercase__    :				Union[str, Any]=	{"total_size": total_size}
						lowercase__    :				List[str]=	{"metadata": metadata, "weight_map": weight_map}

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

						return metadata, index


if __name__ == "__main__":
			a			:    List[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.""",
			)
			a			:    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 						lowercase__()    ->Optional[int]:




						"""simple docstring"""



						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

						lowercase__    :				Tuple=	SwitchTransformersConfig.from_pretrained("google/switch-base-8"						)
						config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"						)
						lowercase__    :				List[Any]=	SwitchTransformersForConditionalGeneration.from_pretrained(
						    "/home/arthur_huggingface_co/transformers/switch_converted" ,		device_map="auto"						)

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

						lowercase__    :				int=	tokenizer(A ,		return_tensors="pt"						).input_ids
						lowercase__    :				List[Any]=	model.generate(A ,		decoder_start_token_id=0						)
						print(tokenizer.decode(out[0]						)						)














"""simple docstring"""







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a			:    Dict				 =		logging.get_logger(__name__)


a			:    Optional[Any]				 =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
a			:    List[str]				 =		{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

a			:    Dict				 =		{
    """allenai/led-base-16384""": 16384,
}





@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						lowercase__()    ->List[Any]:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	(
						    list(range(ord("!"						) ,		ord("~"						) + 1						)						) + list(range(ord("¡"						) ,		ord("¬"						) + 1						)						) + list(range(ord("®"						) ,		ord("ÿ"						) + 1						)						)
						)
						lowercase__    :				str=	bs[:]
						lowercase__    :				List[str]=	0
						for b in range(2**8						):
												if b not in bs:
																		bs.append(A						)
																		cs.append(2**8 + n						)
																		n += 1
						lowercase__    :				Union[str, Any]=	[chr(A						) for n in cs]
						return dict(zip(A ,		A						)						)





def 						lowercase__(A						)    ->str:




						"""simple docstring"""



						lowercase__    :				Optional[int]=	set()
						lowercase__    :				Optional[int]=	word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												lowercase__    :				Tuple=	char
						return pairs







class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= VOCAB_FILES_NAMES
						__lowerCamelCase    	= PRETRAINED_VOCAB_FILES_MAP
						__lowerCamelCase    	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCamelCase    	= ["input_ids", "attention_mask"]




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__="replace"	,  snake_case__="<s>"	,  snake_case__="</s>"	,  snake_case__="</s>"	,  snake_case__="<s>"	,  snake_case__="<unk>"	,  snake_case__="<pad>"	,  snake_case__="<mask>"	,  snake_case__=False	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Dict=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else bos_token
												lowercase__    :				Dict=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else eos_token
												lowercase__    :				str=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else sep_token
												lowercase__    :				Optional[Any]=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else cls_token
												lowercase__    :				List[str]=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else unk_token
												lowercase__    :				Any=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												lowercase__    :				int=	AddedToken(snake_case__	,  lstrip=snake_case__	,  rstrip=snake_case__      ) if isinstance(snake_case__	,  snake_case__      ) else mask_token

												super().__init__(
												    errors=snake_case__	,  bos_token=snake_case__	,  eos_token=snake_case__	,  unk_token=snake_case__	,  sep_token=snake_case__	,  cls_token=snake_case__	,  pad_token=snake_case__	,  mask_token=snake_case__	,  add_prefix_space=snake_case__	,  **snake_case__	,  )

												with open(snake_case__	,  encoding="utf-8"      ) as vocab_handle:
																		lowercase__    :				int=	json.load(snake_case__      )
												lowercase__    :				List[str]=	{v: k for k, v in self.encoder.items()}
												lowercase__    :				Dict=	errors  # how to handle errors in decoding
												lowercase__    :				Optional[int]=	bytes_to_unicode()
												lowercase__    :				str=	{v: k for k, v in self.byte_encoder.items()}
												with open(snake_case__	,  encoding="utf-8"      ) as merges_handle:
																		lowercase__    :				Tuple=	merges_handle.read().split("\n"      )[1:-1]
												lowercase__    :				List[str]=	[tuple(merge.split()      ) for merge in bpe_merges]
												lowercase__    :				int=	dict(zip(snake_case__	,  range(len(snake_case__      )      )      )      )
												lowercase__    :				Optional[int]=	{}
												lowercase__    :				Tuple=	add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												lowercase__    :				Optional[int]=	re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )




						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return len(self.encoder      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return dict(self.encoder	,  **self.added_tokens_encoder      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												if token in self.cache:
																		return self.cache[token]
												lowercase__    :				Any=	tuple(snake_case__      )
												lowercase__    :				Union[str, Any]=	get_pairs(snake_case__      )

												if not pairs:
																		return token

												while True:
																		lowercase__    :				Union[str, Any]=	min(snake_case__	,  key=lambda snake_case__      : self.bpe_ranks.get(snake_case__	,  float("inf"      )      )      )
																		if bigram not in self.bpe_ranks:
																								break
																		lowercase__,     lowercase__    :				Dict=	bigram
																		lowercase__    :				Optional[Any]=	[]
																		lowercase__    :				Optional[int]=	0
																		while i < len(snake_case__      ):
																								try:
																														lowercase__    :				Optional[int]=	word.index(snake_case__	,  snake_case__      )
																								except ValueError:
																														new_word.extend(word[i:]      )
																														break
																								else:
																														new_word.extend(word[i:j]      )
																														lowercase__    :				List[Any]=	j

																								if word[i] == first and i < len(snake_case__      ) - 1 and word[i + 1] == second:
																														new_word.append(first + second      )
																														i += 2
																								else:
																														new_word.append(word[i]      )
																														i += 1
																		lowercase__    :				Optional[int]=	tuple(snake_case__      )
																		lowercase__    :				List[Any]=	new_word
																		if len(snake_case__      ) == 1:
																								break
																		else:
																								lowercase__    :				Tuple=	get_pairs(snake_case__      )
												lowercase__    :				Tuple=	" ".join(snake_case__      )
												lowercase__    :				List[str]=	word
												return word




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	[]
												for token in re.findall(self.pat	,  snake_case__      ):
																		lowercase__    :				int=	"".join(
																		    self.byte_encoder[b] for b in token.encode("utf-8"      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__      ).split(" "      )      )
												return bpe_tokens




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.encoder.get(snake_case__	,  self.encoder.get(self.unk_token      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												return self.decoder.get(snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	"".join(snake_case__      )
												lowercase__    :				Optional[int]=	bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"	,  errors=self.errors      )
												return text




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if not os.path.isdir(snake_case__      ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																		return
												lowercase__    :				Dict=	os.path.join(
												    snake_case__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
												lowercase__    :				Tuple=	os.path.join(
												    snake_case__	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

												with open(snake_case__	,  "w"	,  encoding="utf-8"      ) as f:
																		f.write(json.dumps(self.encoder	,  indent=2	,  sort_keys=snake_case__	,  ensure_ascii=snake_case__      ) + "\n"      )

												lowercase__    :				Any=	0
												with open(snake_case__	,  "w"	,  encoding="utf-8"      ) as writer:
																		writer.write("#version: 0.2\n"      )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,  key=lambda snake_case__      : kv[1]      ):
																								if index != token_index:
																														logger.warning(
																														    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																														    " Please check that the tokenizer is not corrupted!"      )
																														lowercase__    :				Optional[Any]=	token_index
																								writer.write(" ".join(snake_case__      ) + "\n"      )
																								index += 1

												return vocab_file, merge_file




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase__    :				Any=	[self.cls_token_id]
												lowercase__    :				Dict=	[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = False      ):
												'''simple docstring'''
												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=snake_case__	,  token_ids_a=snake_case__	,  already_has_special_tokens=snake_case__      )

												if token_ids_a is None:
																		return [1] + ([0] * len(snake_case__      )) + [1]
												return [1] + ([0] * len(snake_case__      )) + [1, 1] + ([0] * len(snake_case__      )) + [1]




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	[self.sep_token_id]
												lowercase__    :				Optional[int]=	[self.cls_token_id]

												if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__=False	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	kwargs.pop("add_prefix_space"	,  self.add_prefix_space      )
												if (is_split_into_words or add_prefix_space) and (len(snake_case__      ) > 0 and not text[0].isspace()):
																		lowercase__    :				List[Any]=	" " + text
												return (text, kwargs)




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None	,  snake_case__ = PaddingStrategy.DO_NOT_PAD	,  snake_case__ = None	,  snake_case__ = None	,  ):
												'''simple docstring'''
												lowercase__    :				List[str]=	super()._pad(
												    encoded_inputs=snake_case__	,  max_length=snake_case__	,  padding_strategy=snake_case__	,  pad_to_multiple_of=snake_case__	,  return_attention_mask=snake_case__	,  )

												# Load from model defaults
												if return_attention_mask is None:
																		lowercase__    :				Tuple=	"attention_mask" in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowercase__    :				Any=	encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowercase__    :				int=	len(encoded_inputs["global_attention_mask"]      ) != len(snake_case__      )

																		if needs_to_be_padded:
																								lowercase__    :				Dict=	len(snake_case__      ) - len(encoded_inputs["global_attention_mask"]      )

																								if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowercase__    :				Tuple=	(
																														    encoded_inputs["global_attention_mask"] + [-1] * difference
																														)
																								elif self.padding_side == "left":
																														lowercase__    :				Any=	[-1] * difference + encoded_inputs[
																														    "global_attention_mask"
																														]
																								else:
																														raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return encoded_inputs






