



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						List[Any]:
				a__	:							Optional[int]      						= []
				for i in range(config.num_hidden_layers ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'blocks.{i}.norm1.weight', F'deit.encoder.layer.{i}.layernorm_before.weight') )
								rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias') )
								rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight') )
								rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias') )
								rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight') )
								rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias') )
								rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight') )
								rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias') )
								rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight') )
								rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias') )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("cls_token", "deit.embeddings.cls_token"),
				        ("dist_token", "deit.embeddings.distillation_token"),
				        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
				        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
				        ("pos_embed", "deit.embeddings.position_embeddings"),
				    ] )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								        ("pre_logits.fc.weight", "pooler.dense.weight"),
								        ("pre_logits.fc.bias", "pooler.dense.bias"),
								    ] )

								# if just the base model, we should remove "deit" from all keys that start with "deit"
								a__	:							Any      						= [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
				else:
								# layernorm + classification heads
								rename_keys.extend(
								    [
								        ("norm.weight", "deit.layernorm.weight"),
								        ("norm.bias", "deit.layernorm.bias"),
								        ("head.weight", "cls_classifier.weight"),
								        ("head.bias", "cls_classifier.bias"),
								        ("head_dist.weight", "distillation_classifier.weight"),
								        ("head_dist.bias", "distillation_classifier.bias"),
								    ] )

				return rename_keys






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[str]:
				a__	:							int      						= dct.pop(__UpperCamelCase )
				a__	:							Dict      						= val






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






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Union[str, Any]:
				a__	:							List[Any]      						= DeiTConfig()
				# all deit models have fine-tuned heads
				a__	:							Optional[int]      						= False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				a__	:							Optional[int]      						= 10_00
				a__	:							Union[str, Any]      						= "huggingface/label-files"
				a__	:							Tuple      						= "imagenet-1k-id2label.json"
				a__	:							List[Any]      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
				a__	:							Any      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				a__	:							List[str]      						= idalabel
				a__	:							List[Any]      						= {v: k for k, v in idalabel.items()}
				a__	:							Dict      						= int(deit_name[-6:-4] )
				a__	:							str      						= int(deit_name[-3:] )
				# size of the architecture
				if deit_name[9:].startswith("tiny" ):
								a__	:							Optional[int]      						= 1_92
								a__	:							Dict      						= 7_68
								a__	:							List[Any]      						= 12
								a__	:							List[str]      						= 3
				elif deit_name[9:].startswith("small" ):
								a__	:							int      						= 3_84
								a__	:							Any      						= 15_36
								a__	:							Any      						= 12
								a__	:							List[Any]      						= 6
				if deit_name[9:].startswith("base" ):
								pass
				elif deit_name[4:].startswith("large" ):
								a__	:							Optional[Any]      						= 10_24
								a__	:							str      						= 40_96
								a__	:							Tuple      						= 24
								a__	:							Optional[Any]      						= 16

				# load original model from timm
				a__	:							List[Any]      						= timm.create_model(__UpperCamelCase	,				pretrained=__UpperCamelCase )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				a__	:							Union[str, Any]      						= timm_model.state_dict()
				a__	:							List[Any]      						= create_rename_keys(__UpperCamelCase	,				__UpperCamelCase )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				read_in_q_k_v(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

				# load HuggingFace model
				a__	:							Optional[Any]      						= DeiTForImageClassificationWithTeacher(__UpperCamelCase ).eval()
				model.load_state_dict(__UpperCamelCase )

				# Check outputs on an image, prepared by DeiTImageProcessor
				a__	:							Any      						= int(
				    (2_56 / 2_24) * config.image_size )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
				a__	:							Optional[int]      						= DeiTImageProcessor(size=__UpperCamelCase	,				crop_size=config.image_size )
				a__	:							List[Any]      						= image_processor(images=prepare_img()	,				return_tensors="pt" )
				a__	:							Dict      						= encoding["pixel_values"]
				a__	:							Optional[Any]      						= model(__UpperCamelCase )

				a__	:							str      						= timm_model(__UpperCamelCase )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(__UpperCamelCase	,				outputs.logits	,				atol=1e-3 )

				Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
				print(F'Saving model {deit_name} 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 __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--deit_name""",
						    default="""vit_deit_base_distilled_patch16_224""",
						    type=str,
						    help="""Name of the DeiT timm model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)

						lowerCamelCase		       =      parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







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] ) )


