



import argparse
import json
from pathlib import Path

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

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Union[str, Any]:
				a__	:							List[Any]      						= DPTConfig()

				if "large" in checkpoint_url:
								a__	:							Any      						= 10_24
								a__	:							Tuple      						= 40_96
								a__	:							Dict      						= 24
								a__	:							Union[str, Any]      						= 16
								a__	:							Any      						= [5, 11, 17, 23]
								a__	:							Optional[Any]      						= [2_56, 5_12, 10_24, 10_24]
								a__	:							Tuple      						= (1, 3_84, 3_84)

				if "ade" in checkpoint_url:
								a__	:							Optional[Any]      						= True

								a__	:							Dict      						= 1_50
								a__	:							Tuple      						= "huggingface/label-files"
								a__	:							Union[str, Any]      						= "ade20k-id2label.json"
								a__	:							Any      						= json.load(open(cached_download(hf_hub_url(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" ) )	,				"r" ) )
								a__	:							str      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
								a__	:							Any      						= idalabel
								a__	:							Optional[Any]      						= {v: k for k, v in idalabel.items()}
								a__	:							Union[str, Any]      						= [1, 1_50, 4_80, 4_80]

				return config, expected_shape






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							Any      						= ["pretrained.model.head.weight", "pretrained.model.head.bias"]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						str:
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								a__	:							List[Any]      						= name.replace("pretrained.model"	,				"dpt.encoder" )
				if "pretrained.model" in name:
								a__	:							Dict      						= name.replace("pretrained.model"	,				"dpt.embeddings" )
				if "patch_embed" in name:
								a__	:							List[Any]      						= name.replace("patch_embed"	,				"patch_embeddings" )
				if "pos_embed" in name:
								a__	:							Optional[Any]      						= name.replace("pos_embed"	,				"position_embeddings" )
				if "attn.proj" in name:
								a__	:							Dict      						= name.replace("attn.proj"	,				"attention.output.dense" )
				if "proj" in name and "project" not in name:
								a__	:							List[Any]      						= name.replace("proj"	,				"projection" )
				if "blocks" in name:
								a__	:							List[str]      						= name.replace("blocks"	,				"layer" )
				if "mlp.fc1" in name:
								a__	:							Any      						= name.replace("mlp.fc1"	,				"intermediate.dense" )
				if "mlp.fc2" in name:
								a__	:							List[Any]      						= name.replace("mlp.fc2"	,				"output.dense" )
				if "norm1" in name:
								a__	:							Tuple      						= name.replace("norm1"	,				"layernorm_before" )
				if "norm2" in name:
								a__	:							Tuple      						= name.replace("norm2"	,				"layernorm_after" )
				if "scratch.output_conv" in name:
								a__	:							Optional[Any]      						= name.replace("scratch.output_conv"	,				"head" )
				if "scratch" in name:
								a__	:							List[Any]      						= name.replace("scratch"	,				"neck" )
				if "layer1_rn" in name:
								a__	:							str      						= name.replace("layer1_rn"	,				"convs.0" )
				if "layer2_rn" in name:
								a__	:							Optional[int]      						= name.replace("layer2_rn"	,				"convs.1" )
				if "layer3_rn" in name:
								a__	:							Optional[Any]      						= name.replace("layer3_rn"	,				"convs.2" )
				if "layer4_rn" in name:
								a__	:							int      						= name.replace("layer4_rn"	,				"convs.3" )
				if "refinenet" in name:
								a__	:							List[str]      						= int(name[len("neck.refinenet" ) : len("neck.refinenet" ) + 1] )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								a__	:							Tuple      						= name.replace(F'refinenet{layer_idx}'	,				F'fusion_stage.layers.{abs(layer_idx-4 )}' )
				if "out_conv" in name:
								a__	:							str      						= name.replace("out_conv"	,				"projection" )
				if "resConfUnit1" in name:
								a__	:							Optional[Any]      						= name.replace("resConfUnit1"	,				"residual_layer1" )
				if "resConfUnit2" in name:
								a__	:							Optional[int]      						= name.replace("resConfUnit2"	,				"residual_layer2" )
				if "conv1" in name:
								a__	:							List[Any]      						= name.replace("conv1"	,				"convolution1" )
				if "conv2" in name:
								a__	:							Optional[int]      						= name.replace("conv2"	,				"convolution2" )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								a__	:							int      						= name.replace("pretrained.act_postprocess1.0.project.0"	,				"neck.reassemble_stage.readout_projects.0.0" )
				if "pretrained.act_postprocess2.0.project.0" in name:
								a__	:							List[Any]      						= name.replace("pretrained.act_postprocess2.0.project.0"	,				"neck.reassemble_stage.readout_projects.1.0" )
				if "pretrained.act_postprocess3.0.project.0" in name:
								a__	:							Dict      						= name.replace("pretrained.act_postprocess3.0.project.0"	,				"neck.reassemble_stage.readout_projects.2.0" )
				if "pretrained.act_postprocess4.0.project.0" in name:
								a__	:							Union[str, Any]      						= name.replace("pretrained.act_postprocess4.0.project.0"	,				"neck.reassemble_stage.readout_projects.3.0" )
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								a__	:							Dict      						= name.replace("pretrained.act_postprocess1.3"	,				"neck.reassemble_stage.layers.0.projection" )
				if "pretrained.act_postprocess1.4" in name:
								a__	:							List[str]      						= name.replace("pretrained.act_postprocess1.4"	,				"neck.reassemble_stage.layers.0.resize" )
				if "pretrained.act_postprocess2.3" in name:
								a__	:							Optional[Any]      						= name.replace("pretrained.act_postprocess2.3"	,				"neck.reassemble_stage.layers.1.projection" )
				if "pretrained.act_postprocess2.4" in name:
								a__	:							str      						= name.replace("pretrained.act_postprocess2.4"	,				"neck.reassemble_stage.layers.1.resize" )
				if "pretrained.act_postprocess3.3" in name:
								a__	:							Tuple      						= name.replace("pretrained.act_postprocess3.3"	,				"neck.reassemble_stage.layers.2.projection" )
				if "pretrained.act_postprocess4.3" in name:
								a__	:							Any      						= name.replace("pretrained.act_postprocess4.3"	,				"neck.reassemble_stage.layers.3.projection" )
				if "pretrained.act_postprocess4.4" in name:
								a__	:							Any      						= name.replace("pretrained.act_postprocess4.4"	,				"neck.reassemble_stage.layers.3.resize" )
				if "pretrained" in name:
								a__	:							List[Any]      						= name.replace("pretrained"	,				"dpt" )
				if "bn" in name:
								a__	:							List[str]      						= name.replace("bn"	,				"batch_norm" )
				if "head" in name:
								a__	:							Tuple      						= name.replace("head"	,				"head.head" )
				if "encoder.norm" in name:
								a__	:							int      						= name.replace("encoder.norm"	,				"layernorm" )
				if "auxlayer" in name:
								a__	:							Tuple      						= name.replace("auxlayer"	,				"auxiliary_head.head" )

				return name






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Union[str, Any]:
				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								a__	:							Optional[Any]      						= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
								a__	:							str      						= state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							Union[str, Any]      						= in_proj_weight[: config.hidden_size, :]
								a__	:							Tuple      						= in_proj_bias[: config.hidden_size]
								a__	:							str      						= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a__	:							Optional[Any]      						= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a__	:							Any      						= in_proj_weight[
								    -config.hidden_size :, :
								]
								a__	:							str      						= in_proj_bias[-config.hidden_size :]






def     SCREAMING_SNAKE_CASE(      )	->						List[Any]:
				a__	:							Tuple      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							List[str]      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Tuple:
				a__						,   a__	:							List[str]      						= get_dpt_config(__UpperCamelCase )
				# load original state_dict from URL
				a__	:							Tuple      						= torch.hub.load_state_dict_from_url(__UpperCamelCase	,				map_location="cpu" )
				# remove certain keys
				remove_ignore_keys_(__UpperCamelCase )
				# rename keys
				for key in state_dict.copy().keys():
								a__	:							Union[str, Any]      						= state_dict.pop(__UpperCamelCase )
								a__	:							List[Any]      						= val
				# read in qkv matrices
				read_in_q_k_v(__UpperCamelCase	,				__UpperCamelCase )

				# load HuggingFace model
				a__	:							Optional[int]      						= DPTForSemanticSegmentation(__UpperCamelCase ) if "ade" in checkpoint_url else DPTForDepthEstimation(__UpperCamelCase )
				model.load_state_dict(__UpperCamelCase )
				model.eval()

				# Check outputs on an image
				a__	:							Optional[Any]      						= 4_80 if "ade" in checkpoint_url else 3_84
				a__	:							Any      						= DPTImageProcessor(size=__UpperCamelCase )

				a__	:							List[str]      						= prepare_img()
				a__	:							Any      						= image_processor(__UpperCamelCase	,				return_tensors="pt" )

				# forward pass
				a__	:							List[Any]      						= model(**__UpperCamelCase ).logits if "ade" in checkpoint_url else model(**__UpperCamelCase ).predicted_depth

				# Assert logits
				a__	:							List[str]      						= torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
				if "ade" in checkpoint_url:
								a__	:							Optional[int]      						= torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
				assert outputs.shape == torch.Size(__UpperCamelCase )
				assert (
				    torch.allclose(outputs[0, 0, :3, :3]	,				__UpperCamelCase	,				atol=1e-4 )
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3]	,				__UpperCamelCase )
				)

				Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
				print(F'Saving model to {pytorch_dump_folder_path}' )
				model.save_pretrained(__UpperCamelCase )
				print(F'Saving image processor to {pytorch_dump_folder_path}' )
				image_processor.save_pretrained(__UpperCamelCase )

				if push_to_hub:
								print("Pushing model to hub..." )
								model.push_to_hub(
								    repo_path_or_name=Path(__UpperCamelCase	,				__UpperCamelCase )	,				organization="nielsr"	,				commit_message="Add model"	,				use_temp_dir=__UpperCamelCase	,				)
								image_processor.push_to_hub(
								    repo_path_or_name=Path(__UpperCamelCase	,				__UpperCamelCase )	,				organization="nielsr"	,				commit_message="Add image processor"	,				use_temp_dir=__UpperCamelCase	,				)


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
						    type=str,
						    help="""URL of the original DPT checkpoint you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    action="""store_true""",
						)
						parser.add_argument(
						    """--model_name""",
						    default="""dpt-large""",
						    type=str,
						    help="""Name of the model, in case you're pushing to the hub.""",
						)

						lowerCamelCase		       =      parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







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

				return flax_key_tuple, flax_tensor






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if "metadata" in layer:
								a__	:							Union[str, Any]      						= layer.split("metadata" )
								a__	:							Any      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[int]      						= [tuple(("metadata" + split_layer[1]).split("/" ) )]
				elif "kvstore" in layer:
								a__	:							Optional[int]      						= layer.split("kvstore" )
								a__	:							Union[str, Any]      						= "".join(split_layer[0] )[:-1]
								a__	:							Optional[Any]      						= [tuple(("kvstore" + split_layer[1]).split("/" ) )]

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

				if "kvstore/path" in layer:
								a__	:							Tuple      						= F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
				elif "kvstore/driver" in layer:
								a__	:							int      						= "file"
				else:
								a__	:							List[str]      						= checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

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

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

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

								# use the renaming pattern from the small conversion scripts
								a__						,   a__	:							int      						= rename_base_flax_keys(tuple(key.split("/" ) )	,				__UpperCamelCase )
								a__	:							Union[str, Any]      						= "/".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:
												a__	:							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() )
												del current_block
												a__	:							int      						= {}
												a__	:							Optional[int]      						= 0

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

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

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

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				return metadata, index


if __name__ == "__main__":
						lowerCamelCase		       =      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.""",
						)
						lowerCamelCase		       =      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(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


