

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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

							return flax_key_tuple, flax_tensor





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






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

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

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

							return curr_real_layer_name, split_layer, content





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					rename_keys(UpperCamelCase__   )
							snake_case_           =					{}
							for k, v in current_block.items():
														snake_case_           =					v
							snake_case_           =					new_current_block
							torch.save(UpperCamelCase__     ,							UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__ = WEIGHTS_NAME   ):







							'''simple docstring'''






							snake_case_           =					convert_file_size_to_int(UpperCamelCase__   )
							snake_case_           =					[]
							snake_case_           =					{}
							snake_case_           =					0
							snake_case_           =					0

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

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

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

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

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

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

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

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

							return metadata, index


if __name__ == "__main__":
					_UpperCAmelCase	:     Tuple		       = 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.""",
					)
					_UpperCAmelCase	:     List[Any]		       = parser.parse_args()
					shard_on_the_fly(
					    args.switch_tax_checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.max_shard_size,
					    args.dtype,
					)





def       __lowerCamelCase			(				):







							'''simple docstring'''






							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




