

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 :     List[str]						,__a :     Dict      )     ->	List[str]:

					"""simple docstring"""

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

					return flax_key_tuple, flax_tensor



def   __UpperCAmelCase					(      __a :     int						,__a :     List[str]						,__a :     List[str]      )     ->	Optional[int]:

					"""simple docstring"""

					if "metadata" in layer:
										_a      :       Optional[Any]   =						layer.split('''metadata'''      )
										_a      :       Optional[int]   =						''''''.join(split_layer[0]      )[:-1]
										_a      :       Dict   =						[tuple(('''metadata''' + split_layer[1]).split('''/'''      )      )]
					elif "kvstore" in layer:
										_a      :       List[Any]   =						layer.split('''kvstore'''      )
										_a      :       List[str]   =						''''''.join(split_layer[0]      )[:-1]
										_a      :       str   =						[tuple(('''kvstore''' + split_layer[1]).split('''/'''      )      )]

					else:
										_a      :       Tuple   =						layer.split('''/'''      )
										_a      :       Any   =						'''/'''.join(split_layer[:-1]      )
										_a      :       Optional[Any]   =						(split_layer[-1],)

					if "kvstore/path" in layer:
										_a      :       Tuple   =						F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
					elif "kvstore/driver" in layer:
										_a      :       str   =						'''file'''
					else:
										_a      :       Optional[Any]   =						checkpoint_info[layer]

					return curr_real_layer_name, split_layer, content



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       int   =						rename_keys(__a      )
					_a      :       Any   =						{}
					for k, v in current_block.items():
										_a      :       Dict   =						v
					_a      :       int   =						new_current_block
					torch.save(__a						,__a      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     int						,__a :     str						,__a :     int						,__a :     str = WEIGHTS_NAME      )     ->	Any:

					"""simple docstring"""

					_a      :       Optional[Any]   =						convert_file_size_to_int(__a      )
					_a      :       List[str]   =						[]
					_a      :       List[Any]   =						{}
					_a      :       int   =						0
					_a      :       List[Any]   =						0

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

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

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

										# use the renaming pattern from the small conversion scripts
										_a ,   _a      :       str   =						rename_base_flax_keys(tuple(key.split('''/'''      )      )						,__a      )
										_a      :       int   =						'''/'''.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      :       Union[str, Any]   =						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      :       List[Any]   =						{}
															_a      :       Optional[int]   =						0

										_a      :       Dict   =						raw_weights.to(getattr(__a						,__a      )      )
										current_block_size += weight_size
										total_size += weight_size

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

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

					with open(os.path.join(__a						,__a      )						,'''w'''						,encoding='''utf-8'''      ) as f:
										_a      :       Optional[int]   =						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					(      )     ->	Dict:

					"""simple docstring"""

					from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

					_a      :       Any   =						TaTokenizer.from_pretrained('''t5-small'''      )
					_a      :       Optional[int]   =						'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

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






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Dict						,__a :     List[str]						,__a :     Optional[Any]						,__a :     Tuple      )     ->	Dict:

					"""simple docstring"""

					with open(__a      ) as metadata_file:
										_a      :       Optional[Any]   =						json.load(__a      )
					_a      :       List[Any]   =						LukeConfig(use_entity_aware_attention=__a						,**metadata['''model_config''']      )

					# Load in the weights from the checkpoint_path
					_a      :       Optional[Any]   =						torch.load(__a						,map_location='''cpu'''      )['''module''']

					# Load the entity vocab file
					_a      :       Any   =						load_original_entity_vocab(__a      )
					# add an entry for [MASK2]
					_a      :       Union[str, Any]   =						max(entity_vocab.values()      ) + 1
					config.entity_vocab_size += 1

					_a      :       Dict   =						XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

					# Add special tokens to the token vocabulary for downstream tasks
					_a      :       Optional[int]   =						AddedToken('''<ent>'''						,lstrip=__a						,rstrip=__a      )
					_a      :       Tuple   =						AddedToken('''<ent2>'''						,lstrip=__a						,rstrip=__a      )
					tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}      )
					config.vocab_size += 2

					print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""      )
					tokenizer.save_pretrained(__a      )
					with open(os.path.join(__a						,'''tokenizer_config.json'''      )						,'''r'''      ) as f:
										_a      :       List[str]   =						json.load(__a      )
					_a      :       Tuple   =						'''MLukeTokenizer'''
					with open(os.path.join(__a						,'''tokenizer_config.json'''      )						,'''w'''      ) as f:
										json.dump(__a						,__a      )

					with open(os.path.join(__a						,MLukeTokenizer.vocab_files_names['''entity_vocab_file''']      )						,'''w'''      ) as f:
										json.dump(__a						,__a      )

					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )

					# Initialize the embeddings of the special tokens
					_a      :       str   =						tokenizer.convert_tokens_to_ids(['''@''']      )[0]
					_a      :       Tuple   =						tokenizer.convert_tokens_to_ids(['''#''']      )[0]

					_a      :       Any   =						state_dict['''embeddings.word_embeddings.weight''']
					_a      :       Optional[int]   =						word_emb[ent_init_index].unsqueeze(0      )
					_a      :       Any   =						word_emb[enta_init_index].unsqueeze(0      )
					_a      :       Union[str, Any]   =						torch.cat([word_emb, ent_emb, enta_emb]      )
					# add special tokens for 'entity_predictions.bias'
					for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
										_a      :       Tuple   =						state_dict[bias_name]
										_a      :       Optional[Any]   =						decoder_bias[ent_init_index].unsqueeze(0      )
										_a      :       Optional[int]   =						decoder_bias[enta_init_index].unsqueeze(0      )
										_a      :       Dict   =						torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

					# Initialize the query layers of the entity-aware self-attention mechanism
					for layer_index in range(config.num_hidden_layers      ):
										for matrix_name in ["query.weight", "query.bias"]:
															_a      :       Tuple   =						F"""encoder.layer.{layer_index}.attention.self."""
															_a      :       List[Any]   =						state_dict[prefix + matrix_name]
															_a      :       Dict   =						state_dict[prefix + matrix_name]
															_a      :       List[Any]   =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
					_a      :       Union[str, Any]   =						state_dict['''entity_embeddings.entity_embeddings.weight''']
					_a      :       Optional[int]   =						entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0      )
					_a      :       Any   =						torch.cat([entity_emb, entity_mask_emb]      )
					# add [MASK2] for 'entity_predictions.bias'
					_a      :       int   =						state_dict['''entity_predictions.bias''']
					_a      :       int   =						entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0      )
					_a      :       Optional[Any]   =						torch.cat([entity_prediction_bias, entity_mask_bias]      )

					_a      :       Optional[int]   =						LukeForMaskedLM(config=__a      ).eval()

					state_dict.pop('''entity_predictions.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.weight'''      )
					state_dict.pop('''lm_head.decoder.bias'''      )
					_a      :       int   =						OrderedDict()
					for key, value in state_dict.items():
										if not (key.startswith('''lm_head'''      ) or key.startswith('''entity_predictions'''      )):
															_a      :       Optional[int]   =						state_dict[key]
										else:
															_a      :       Tuple   =						state_dict[key]

					_a ,   _a      :       int   =						model.load_state_dict(__a						,strict=__a      )

					if set(__a      ) != {"luke.embeddings.position_ids"}:
										raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""      )
					if set(__a      ) != {
					    "lm_head.decoder.weight",
					    "lm_head.decoder.bias",
					    "entity_predictions.decoder.weight",
					}:
										raise ValueError(F"""Unexpected missing_keys: {missing_keys}"""      )

					model.tie_weights()
					assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
					assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

					# Check outputs
					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a						,task='''entity_classification'''      )

					_a      :       int   =						'''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
					_a      :       List[Any]   =						(0, 9)
					_a      :       Tuple   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       List[str]   =						torch.Size((1, 33, 768)      )
										_a      :       Union[str, Any]   =						torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]      )

					if not (outputs.last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""      )
					if not torch.allclose(outputs.last_hidden_state[0, :3, :3]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify entity hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										_a      :       str   =						torch.Size((1, 1, 768)      )
										_a      :       List[Any]   =						torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]      )

					if not (outputs.entity_last_hidden_state.shape == expected_shape):
										raise ValueError(
										    F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
										    F""" {expected_shape}"""      )
					if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]						,__a						,atol=1E-4      ):
										raise ValueError

					# Verify masked word/entity prediction
					_a      :       Optional[int]   =						MLukeTokenizer.from_pretrained(__a      )
					_a      :       Dict   =						'''Tokyo is the capital of <mask>.'''
					_a      :       List[str]   =						(24, 30)
					_a      :       Optional[int]   =						tokenizer(__a						,entity_spans=[span]						,return_tensors='''pt'''      )

					_a      :       Optional[Any]   =						model(**__a      )

					_a      :       Any   =						encoding['''input_ids'''][0].tolist()
					_a      :       Optional[Any]   =						input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''      )      )
					_a      :       Any   =						outputs.logits[0][mask_position_id].argmax(dim=-1      )
					assert "Japan" == tokenizer.decode(__a      )

					_a      :       Any   =						outputs.entity_logits[0][0].argmax().item()
					_a      :       Optional[Any]   =						[
					    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
					]
					assert [e for e in multilingual_predicted_entities if e.startswith('''en:'''      )][0] == "en:Japan"

					# Finally, save our PyTorch model and tokenizer
					print('''Saving PyTorch model to {}'''.format(__a      )      )
					model.save_pretrained(__a      )



def   __UpperCAmelCase					(      __a :     List[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						['''[MASK]''', '''[PAD]''', '''[UNK]''']

					_a      :       int   =						[json.loads(__a      ) for line in open(__a      )]

					_a      :       List[Any]   =						{}
					for entry in data:
										_a      :       int   =						entry['''id''']
										for entity_name, language in entry["entities"]:
															if entity_name in SPECIAL_TOKENS:
																				_a      :       List[Any]   =						entity_id
																				break
															_a      :       Dict   =						F"""{language}:{entity_name}"""
															_a      :       int   =						entity_id
					return new_mapping


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			a__												=						parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)



