
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			List[str]    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    SwinConfig(
				    embed_dim=1_92  ,     depths=(2, 2, 18, 2)  ,     num_heads=(6, 12, 24, 48)  ,     window_size=12  ,     out_features=["""stage2""", """stage3""", """stage4"""]  ,     )

				UpperCamelCase_:				Dict						=    DetaConfig(
				    backbone_config=lowerCamelCase  ,     num_queries=9_00  ,     encoder_ffn_dim=20_48  ,     decoder_ffn_dim=20_48  ,     num_feature_levels=5  ,     assign_first_stage=lowerCamelCase  ,     with_box_refine=lowerCamelCase  ,     two_stage=lowerCamelCase  ,     )

				# set labels
				UpperCamelCase_:				Dict						=    "huggingface/label-files"
				if "o365" in model_name:
								UpperCamelCase_:				Optional[Any]						=    3_66
								UpperCamelCase_:				Union[str, Any]						=    "object365-id2label.json"
				else:
								UpperCamelCase_:				List[str]						=    91
								UpperCamelCase_:				str						=    "coco-detection-id2label.json"

				UpperCamelCase_:				List[str]						=    num_labels
				UpperCamelCase_:				List[str]						=    json.load(open(cached_download(hf_hub_url(lowerCamelCase  ,     lowerCamelCase  ,     repo_type="""dataset"""    )    )  ,     """r"""    )    )
				UpperCamelCase_:				Optional[Any]						=    {int(lowerCamelCase    ): v for k, v in idalabel.items()}
				UpperCamelCase_:				Optional[int]						=    idalabel
				UpperCamelCase_:				str						=    {v: k for k, v in idalabel.items()}

				return config







def 							A__       (    lowerCamelCase    )	->						Any:
				UpperCamelCase_:				Tuple						=    []

				# stem
				# fmt: off
				rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")    )
				rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")    )
				rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")    )
				rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")    )
				# stages
				for i in range(len(config.backbone_config.depths    )    ):
								for j in range(config.backbone_config.depths[i]    ):
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')    )

								if i < 3:
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')    )
												rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')    )

				rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")    )
				rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")    )
				rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")    )
				rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")    )
				rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")    )
				rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")    )

				# transformer encoder
				for i in range(config.encoder_layers    ):
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''')    )
								rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''')    )

				# transformer decoder
				for i in range(config.decoder_layers    ):
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''')    )
								rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''')    )

				# fmt: on

				return rename_keys







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				UpperCamelCase_:				List[Any]						=    dct.pop(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    val







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				int						=    [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
				for i in range(len(backbone_config.depths    )    ):
								UpperCamelCase_:				int						=    num_features[i]
								for j in range(backbone_config.depths[i]    ):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												UpperCamelCase_:				List[Any]						=    state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''    )
												UpperCamelCase_:				Tuple						=    state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''    )
												# next, add query, keys and values (in that order) to the state dict
												UpperCamelCase_:				List[Any]						=    in_proj_weight[:dim, :]
												UpperCamelCase_:				Any						=    in_proj_bias[: dim]
												UpperCamelCase_:				Tuple						=    in_proj_weight[
												    dim : dim * 2, :
												]
												UpperCamelCase_:				Any						=    in_proj_bias[
												    dim : dim * 2
												]
												UpperCamelCase_:				Tuple						=    in_proj_weight[
												    -dim :, :
												]
												UpperCamelCase_:				Optional[Any]						=    in_proj_bias[-dim :]
												# fmt: on







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				UpperCamelCase_:				Union[str, Any]						=    config.d_model
				for i in range(config.decoder_layers    ):
								# read in weights + bias of input projection layer of self-attention
								UpperCamelCase_:				Any						=    state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''    )
								UpperCamelCase_:				Optional[Any]						=    state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''    )
								# next, add query, keys and values (in that order) to the state dict
								UpperCamelCase_:				Dict						=    in_proj_weight[:hidden_size, :]
								UpperCamelCase_:				Any						=    in_proj_bias[:hidden_size]
								UpperCamelCase_:				List[Any]						=    in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								UpperCamelCase_:				List[str]						=    in_proj_bias[hidden_size : hidden_size * 2]
								UpperCamelCase_:				Any						=    in_proj_weight[-hidden_size:, :]
								UpperCamelCase_:				Optional[int]						=    in_proj_bias[-hidden_size:]







def 							A__       (    )	->						Any:
				UpperCamelCase_:				Any						=    "http://images.cocodataset.org/val2017/000000039769.jpg"
				UpperCamelCase_:				Any						=    Image.open(requests.get(lowerCamelCase  ,     stream=lowerCamelCase    ).raw    )

				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						int:
				UpperCamelCase_:				Dict						=    get_deta_config(lowerCamelCase    )

				# load original state dict
				if model_name == "deta-swin-large":
								UpperCamelCase_:				Dict						=    hf_hub_download(repo_id="""nielsr/deta-checkpoints"""  ,     filename="""adet_swin_ft.pth"""    )
				elif model_name == "deta-swin-large-o365":
								UpperCamelCase_:				str						=    hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""  ,     filename="""deta_swin_pt_o365.pth"""    )
				else:
								raise ValueError(F'''Model name {model_name} not supported'''    )

				UpperCamelCase_:				int						=    torch.load(lowerCamelCase  ,     map_location="""cpu"""    )["model"]

				# original state dict
				for name, param in state_dict.items():
								print(lowerCamelCase  ,     param.shape    )

				# rename keys
				UpperCamelCase_:				Optional[Any]						=    create_rename_keys(lowerCamelCase    )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
				read_in_swin_q_k_v(lowerCamelCase  ,     config.backbone_config    )
				read_in_decoder_q_k_v(lowerCamelCase  ,     lowerCamelCase    )

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												UpperCamelCase_:				List[Any]						=    state_dict.pop(lowerCamelCase    )
												UpperCamelCase_:				Optional[int]						=    val
								if "input_proj" in key:
												UpperCamelCase_:				Tuple						=    state_dict.pop(lowerCamelCase    )
												UpperCamelCase_:				Optional[int]						=    val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												UpperCamelCase_:				Dict						=    state_dict.pop(lowerCamelCase    )
												UpperCamelCase_:				Optional[Any]						=    val

    # finally, create HuggingFace model and load state dict
				UpperCamelCase_:				Optional[int]						=    DetaForObjectDetection(lowerCamelCase    )
				model.load_state_dict(lowerCamelCase    )
				model.eval()

				UpperCamelCase_:				Tuple						=    "cuda" if torch.cuda.is_available() else "cpu"
				model.to(lowerCamelCase    )

				# load image processor
				UpperCamelCase_:				Dict						=    DetaImageProcessor(format="""coco_detection"""    )

				# verify our conversion on image
				UpperCamelCase_:				Optional[int]						=    prepare_img()
				UpperCamelCase_:				Any						=    processor(images=lowerCamelCase  ,     return_tensors="""pt"""    )
				UpperCamelCase_:				List[str]						=    encoding["pixel_values"]
				UpperCamelCase_:				List[str]						=    model(pixel_values.to(lowerCamelCase    )    )

				# verify logits
				print("""Logits:"""  ,     outputs.logits[0, :3, :3]    )
				print("""Boxes:"""  ,     outputs.pred_boxes[0, :3, :3]    )
				if model_name == "deta-swin-large":
								UpperCamelCase_:				int						=    torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]    )
								UpperCamelCase_:				Dict						=    torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]    )
				elif model_name == "deta-swin-large-o365":
								UpperCamelCase_:				Tuple						=    torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]    )
								UpperCamelCase_:				Optional[int]						=    torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]    )
				assert torch.allclose(outputs.logits[0, :3, :3]  ,     expected_logits.to(lowerCamelCase    )  ,     atol=1E-4    )
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,     expected_boxes.to(lowerCamelCase    )  ,     atol=1E-4    )
				print("""Everything ok!"""    )

				if pytorch_dump_folder_path:
								# Save model and processor
								logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''    )
								Path(lowerCamelCase    ).mkdir(exist_ok=lowerCamelCase    )
								model.save_pretrained(lowerCamelCase    )
								processor.save_pretrained(lowerCamelCase    )

				# Push to hub
				if push_to_hub:
								print("""Pushing model and processor to hub..."""    )
								model.push_to_hub(F'''jozhang97/{model_name}'''    )
								processor.push_to_hub(F'''jozhang97/{model_name}'''    )


if __name__ == "__main__":
							lowerCamelCase_    :			Optional[Any]    =	argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    type=str,
							    default="""deta-swin-large""",
							    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
							    help="""Name of the model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    help="""Path to the folder to output PyTorch model.""",
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)
							lowerCamelCase_    :			Optional[Any]    =	parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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 ):



		'''simple docstring'''




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

		return flax_key_tuple, flax_tensor


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




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

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

		if "kvstore/path" in layer:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
		elif "kvstore/driver" in layer:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						"file"
		else:
				SCREAMING_SNAKE_CASE_				:str				     =						checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						rename_keys(a )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{}
		for k, v in current_block.items():
				SCREAMING_SNAKE_CASE_				:List[str]				     =						v
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						new_current_block
		torch.save(a     , a )


def     lowercase	(    a     , a     , a     , a     , a = WEIGHTS_NAME ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						convert_file_size_to_int(a )
		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:str				     =						{}
		SCREAMING_SNAKE_CASE_				:List[str]				     =						0
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0

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

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{}
		for layer in checkpoint_info.keys():
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						get_key_and_tensorstore_dict(
				    a     , a     , a )
				if curr_real_layer_name in all_layers:
						SCREAMING_SNAKE_CASE_				:str				     =						content
				else:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{split_layer[-1]: content}

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

				# use the renaming pattern from the small conversion scripts
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						rename_base_flax_keys(tuple(key.split("/" ) )     , a )
				SCREAMING_SNAKE_CASE_				: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:
						SCREAMING_SNAKE_CASE_				:str				     =						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
						SCREAMING_SNAKE_CASE_				:Tuple				     =						{}
						SCREAMING_SNAKE_CASE_				:Dict				     =						0

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

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

    # Add the metadata
		SCREAMING_SNAKE_CASE_				:List[str]				     =						{"total_size": total_size}
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(a     , a )     , "w"     , encoding="utf-8" ) as f:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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     lowercase	(    ):



		'''simple docstring'''




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

		SCREAMING_SNAKE_CASE_				:Dict				     =						SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
		config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
		SCREAMING_SNAKE_CASE_				:str				     =						SwitchTransformersForConditionalGeneration.from_pretrained(
		    "/home/arthur_huggingface_co/transformers/switch_converted"     , device_map="auto" )

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						TaTokenizer.from_pretrained("t5-small" )
		SCREAMING_SNAKE_CASE_				: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>."

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						tokenizer(a     , return_tensors="pt" ).input_ids
		SCREAMING_SNAKE_CASE_				:List[str]				     =						model.generate(a     , decoder_start_token_id=0 )
		print(tokenizer.decode(out[0] ) )






