
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 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[Any]    ,							SCREAMING_SNAKE_CASE       :      Optional[int]			)     ->						List[Any]:






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

				return flax_key_tuple, flax_tensor




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[str]    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]    ,							SCREAMING_SNAKE_CASE       :      str			)     ->						Union[str, Any]:






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

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

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

				return curr_real_layer_name, split_layer, content




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Optional[Any]    ,							SCREAMING_SNAKE_CASE       :      List[Any]			)     ->						int:
				__lowercase								=					rename_keys(__a			)
				__lowercase								=					{}
				for k, v in current_block.items():
								__lowercase								=					v
				__lowercase								=					new_current_block
				torch.save(__a    ,							__a			)




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      Any    ,							SCREAMING_SNAKE_CASE       :      str    ,							SCREAMING_SNAKE_CASE       :      Optional[int]    ,							SCREAMING_SNAKE_CASE       :      List[Any] = WEIGHTS_NAME			)     ->						Dict:
				__lowercase								=					convert_file_size_to_int(__a			)
				__lowercase								=					[]
				__lowercase								=					{}
				__lowercase								=					0
				__lowercase								=					0

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

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

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

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

								__lowercase								=					raw_weights.to(getattr(__a    ,							__a			)			)
								current_block_size += weight_size
								total_size += weight_size

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

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

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

				return metadata, index


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




def 							__SCREAMING_SNAKE_CASE   (		)     ->						Tuple:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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













"""simple docstring"""






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)


