




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   _snake_case    (       lowercase__							,					lowercase__	):






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

				return flax_key_tuple, flax_tensor







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):






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

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

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

				return curr_real_layer_name, split_layer, content







def   _snake_case    (       lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					rename_keys(UpperCamelCase__	)
				_lowerCamelCase  :    Optional[Any]					=					{}
				for k, v in current_block.items():
								_lowerCamelCase  :    List[Any]					=					v
				_lowerCamelCase  :    Union[str, Any]					=					new_current_block
				torch.save(UpperCamelCase__							,					UpperCamelCase__	)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					lowercase__ = WEIGHTS_NAME	):
				_lowerCamelCase  :    List[str]					=					convert_file_size_to_int(UpperCamelCase__	)
				_lowerCamelCase  :    Optional[int]					=					[]
				_lowerCamelCase  :    int					=					{}
				_lowerCamelCase  :    Optional[Any]					=					0
				_lowerCamelCase  :    int					=					0

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

				_lowerCamelCase  :    int					=					{}
				for layer in checkpoint_info.keys():
								_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					get_key_and_tensorstore_dict(
								    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__	)
								if curr_real_layer_name in all_layers:
												_lowerCamelCase  :    Dict					=					content
								else:
												_lowerCamelCase  :    List[str]					=					{split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								_lowerCamelCase  :    Dict					=					ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
								_lowerCamelCase  :    Union[str, Any]					=					torch.tensor(UpperCamelCase__	)
								_lowerCamelCase  :    Optional[int]					=					raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

								# use the renaming pattern from the small conversion scripts
								_lowerCamelCase,				_lowerCamelCase  :    Any					=					rename_base_flax_keys(tuple(key.split('/'	)	)							,					UpperCamelCase__	)
								_lowerCamelCase  :    Tuple					=					'/'.join(UpperCamelCase__	)

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												_lowerCamelCase  :    Optional[Any]					=					os.path.join(
												    UpperCamelCase__							,					weights_name.replace('.bin'							,					f'''-{len(UpperCamelCase__	)+1:05d}-of-???.bin'''	)	)
												rename_and_save_block(UpperCamelCase__							,					UpperCamelCase__	)
												sharded_state_dicts.append(current_block.keys()	)
												del current_block
												_lowerCamelCase  :    Optional[int]					=					{}
												_lowerCamelCase  :    Tuple					=					0

								_lowerCamelCase  :    str					=					raw_weights.to(getattr(UpperCamelCase__							,					UpperCamelCase__	)	)
								current_block_size += weight_size
								total_size += weight_size

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

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

				# Otherwise, let's build the index
				_lowerCamelCase  :    Any					=					{}
				_lowerCamelCase  :    Any					=					{}
				for idx, shard in enumerate(UpperCamelCase__	):
								_lowerCamelCase  :    Dict					=					weights_name.replace(
								    '.bin'							,					f'''-{idx+1:05d}-of-{len(UpperCamelCase__	):05d}.bin'''	)  # len(sharded_state_dicts):05d}
								_lowerCamelCase  :    List[str]					=					os.path.join(UpperCamelCase__							,					weights_name.replace('.bin'							,					f'''-{idx+1:05d}-of-???.bin'''	)	)
								os.rename(UpperCamelCase__							,					os.path.join(UpperCamelCase__							,					UpperCamelCase__	)	)
								_lowerCamelCase  :    Union[str, Any]					=					shard
								for key in shard:
												_lowerCamelCase  :    List[Any]					=					shard_file

    # Add the metadata
				_lowerCamelCase  :    List[Any]					=					{'total_size': total_size}
				_lowerCamelCase  :    Optional[Any]					=					{'metadata': metadata, 'weight_map': weight_map}

				with open(os.path.join(UpperCamelCase__							,					UpperCamelCase__	)							,					'w'							,					encoding='utf-8'	) as f:
								_lowerCamelCase  :    List[Any]					=					json.dumps(UpperCamelCase__							,					indent=2							,					sort_keys=UpperCamelCase__	) + '\n'
								f.write(UpperCamelCase__	)

				return metadata, index


if __name__ == "__main__":
			lowercase__  		=		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.""",
			)
			lowercase__  		=		parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)







def   _snake_case    (       ):
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				_lowerCamelCase  :    Tuple					=					tokenizer(UpperCamelCase__							,					return_tensors='pt'	).input_ids
				_lowerCamelCase  :    List[Any]					=					model.generate(UpperCamelCase__							,					decoder_start_token_id=0	)
				print(tokenizer.decode(out[0]	)	)





"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """umt5"""
			lowerCamelCase__   =    ["""past_key_values"""]
			def __init__(						self						,   lowercase=250112						,   lowercase=512						,   lowercase=64						,   lowercase=1024						,   lowercase=8						,   lowercase=None						,   lowercase=6						,   lowercase=32						,   lowercase=128						,   lowercase=0.1						,   lowercase=1E-6						,   lowercase=1.0						,   lowercase="gated-gelu"						,   lowercase=True						,   lowercase=True						,   lowercase="T5Tokenizer"						,   lowercase=True						,   lowercase=0						,   lowercase=1						,   lowercase=0						,   **lowercase						,   ):
							super().__init__(
							    is_encoder_decoder=lowercase						,   tokenizer_class=lowercase						,   tie_word_embeddings=lowercase						,   pad_token_id=lowercase						,   eos_token_id=lowercase						,   decoder_start_token_id=lowercase						,   **lowercase						,   )
							_lowerCamelCase  :    Union[str, Any]					=					vocab_size
							_lowerCamelCase  :    Optional[Any]					=					d_model
							_lowerCamelCase  :    Optional[Any]					=					d_kv
							_lowerCamelCase  :    Optional[int]					=					d_ff
							_lowerCamelCase  :    List[str]					=					num_layers
							_lowerCamelCase  :    Union[str, Any]					=					(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							_lowerCamelCase  :    List[str]					=					num_heads
							_lowerCamelCase  :    int					=					relative_attention_num_buckets
							_lowerCamelCase  :    Union[str, Any]					=					relative_attention_max_distance
							_lowerCamelCase  :    int					=					dropout_rate
							_lowerCamelCase  :    List[Any]					=					layer_norm_epsilon
							_lowerCamelCase  :    Union[str, Any]					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					feed_forward_proj
							_lowerCamelCase  :    Dict					=					use_cache

							_lowerCamelCase  :    Dict					=					self.feed_forward_proj.split('-'						)
							_lowerCamelCase  :    List[str]					=					act_info[-1]
							_lowerCamelCase  :    int					=					act_info[0] == 'gated'

							if len(lowercase						) > 1 and act_info[0] != "gated" or len(lowercase						) > 2:
											raise ValueError(
											    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''						)

							if feed_forward_proj == "gated-gelu":
											_lowerCamelCase  :    Optional[int]					=					'gelu_new'
			@property
			def       A_					(						self						):
							return self.d_model
			@property
			def       A_					(						self						):
							return self.num_heads






			@property
			def       A_					(						self						):
							return self.num_layers






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
											_lowerCamelCase  :    Optional[int]					=					'past_encoder_sequence + sequence'
											_lowerCamelCase  :    int					=					{0: 'batch'}
											_lowerCamelCase  :    List[str]					=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
											_lowerCamelCase  :    Dict					=					{0: 'batch', 1: 'decoder_sequence'}
											_lowerCamelCase  :    str					=					{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
											self.fill_with_past_key_values_(lowercase						,   direction='inputs'						)

							return common_inputs
			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def       A_					(						self						):
							return 13






			@property
			def       A_					(						self						):
							return 5E-4