

import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_ :				str						 =    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_ :				Tuple						 =    []
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append(
	    (
	        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
	        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
	        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
	    )
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Dict					,   lowercase_:				Optional[int]      )  ->   Union[str, Any]:
	A__							:					List[Any]       =	state_dict.pop(lowercase_      )
	A__							:					Tuple       =	val

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   List[str]:
	A__							:					int       =	OrderedDict()
	for key, value in state_dict.items():
		if "backbone.0.body" in key:
			A__							:					int       =	key.replace("""backbone.0.body"""					,   """backbone.conv_encoder.model"""      )
			A__							:					Any       =	value
		else:
			A__							:					Optional[int]       =	value

	return new_state_dict

def 		UpperCamelCase      (lowercase_:				Optional[int]					,   lowercase_:				Dict=False      )  ->   Optional[int]:
	A__							:					List[str]       =	""""""
	if is_panoptic:
		A__							:					List[Any]       =	"""conditional_detr."""

	# first: transformer encoder
	for i in range(6      ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		A__							:					int       =	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
		A__							:					List[Any]       =	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""      )
		# next, add query, keys and values (in that order) to the state dict
		A__							:					Dict       =	in_proj_weight[:256, :]
		A__							:					List[Any]       =	in_proj_bias[:256]
		A__							:					Optional[Any]       =	in_proj_weight[256:512, :]
		A__							:					Tuple       =	in_proj_bias[256:512]
		A__							:					Dict       =	in_proj_weight[-256:, :]
		A__							:					Dict       =	in_proj_bias[-256:]

def 		UpperCamelCase      ()  ->   List[Any]:
	A__							:					List[str]       =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:					Optional[int]       =	Image.open(requests.get(lowercase_					,   stream=lowercase_      ).raw      )

	return im

@torch.no_grad()
def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				int      )  ->   int:
	A__							:					Optional[Any]       =	ConditionalDetrConfig()
	# set backbone and dilation attributes
	if "resnet101" in model_name:
		A__							:					Optional[int]       =	"""resnet101"""
	if "dc5" in model_name:
		A__							:					str       =	True
	A__							:					Union[str, Any]       =	"""panoptic""" in model_name
	if is_panoptic:
		A__							:					Tuple       =	250
	else:
		A__							:					Tuple       =	91
		A__							:					Union[str, Any]       =	"""huggingface/label-files"""
		A__							:					str       =	"""coco-detection-id2label.json"""
		A__							:					Tuple       =	json.load(open(hf_hub_download(lowercase_					,   lowercase_					,   repo_type="""dataset"""      )					,   """r"""      )      )
		A__							:					List[Any]       =	{int(lowercase_      ): v for k, v in idalabel.items()}
		A__							:					Optional[Any]       =	idalabel
		A__							:					Union[str, Any]       =	{v: k for k, v in idalabel.items()}

	# load image processor
	A__							:					str       =	"""coco_panoptic""" if is_panoptic else """coco_detection"""
	A__							:					List[Any]       =	ConditionalDetrImageProcessor(format=lowercase_      )

	# prepare image
	A__							:					Union[str, Any]       =	prepare_img()
	A__							:					Any       =	image_processor(images=lowercase_					,   return_tensors="""pt"""      )
	A__							:					int       =	encoding["""pixel_values"""]

	logger.info(f"""Converting model {model_name}..."""      )

	# load original model from torch hub
	A__							:					Any       =	torch.hub.load("""DeppMeng/ConditionalDETR"""					,   lowercase_					,   pretrained=lowercase_      ).eval()
	A__							:					int       =	conditional_detr.state_dict()
	# rename keys
	for src, dest in rename_keys:
		if is_panoptic:
			A__							:					List[str]       =	"""conditional_detr.""" + src
		rename_key(lowercase_					,   lowercase_					,   lowercase_      )
	A__							:					int       =	rename_backbone_keys(lowercase_      )
	# query, key and value matrices need special treatment
	read_in_q_k_v(lowercase_					,   is_panoptic=lowercase_      )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	A__							:					Union[str, Any]       =	"""conditional_detr.model.""" if is_panoptic else """model."""
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith("""conditional_detr"""      )
			    and not key.startswith("""class_labels_classifier"""      )
			    and not key.startswith("""bbox_predictor"""      )
			):
				A__							:					List[Any]       =	state_dict.pop(lowercase_      )
				A__							:					int       =	val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				A__							:					Dict       =	state_dict.pop(lowercase_      )
				A__							:					int       =	val
			elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
				continue
			else:
				A__							:					Optional[int]       =	state_dict.pop(lowercase_      )
				A__							:					List[str]       =	val
		else:
			if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
				A__							:					Dict       =	state_dict.pop(lowercase_      )
				A__							:					str       =	val
    # finally, create HuggingFace model and load state dict
	A__							:					Optional[int]       =	ConditionalDetrForSegmentation(lowercase_      ) if is_panoptic else ConditionalDetrForObjectDetection(lowercase_      )
	model.load_state_dict(lowercase_      )
	model.eval()
	model.push_to_hub(repo_id=lowercase_					,   organization="""DepuMeng"""					,   commit_message="""Add model"""      )
	# verify our conversion
	A__							:					Tuple       =	conditional_detr(lowercase_      )
	A__							:					Tuple       =	model(lowercase_      )
	assert torch.allclose(outputs.logits					,   original_outputs["""pred_logits"""]					,   atol=1E-4      )
	assert torch.allclose(outputs.pred_boxes					,   original_outputs["""pred_boxes"""]					,   atol=1E-4      )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks					,   original_outputs["""pred_masks"""]					,   atol=1E-4      )

	# Save model and image processor
	logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""      )
	Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
	model.save_pretrained(lowercase_      )
	image_processor.save_pretrained(lowercase_      )


if __name__ == "__main__":
	A_ :				Union[str, Any]						 =    argparse.ArgumentParser()

	parser.add_argument(
	    '--model_name',
	    default='conditional_detr_resnet50',
	    type=str,
	    help='Name of the CONDITIONAL_DETR 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.'
	)
	A_ :				Union[str, Any]						 =    parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_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 		UpperCamelCase      (lowercase_:				List[str]					,   lowercase_:				str      )  ->   Optional[Any]:
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		A__							:					Union[str, Any]       =	flax_key_tuple[:-1] + ("""weight""",)
		A__							:					Optional[int]       =	torch.permute(lowercase_					,   (0, 2, 1)      )
	elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase_      ):
		# linear layer
		A__							:					Optional[Any]       =	flax_key_tuple[:-1] + ("""weight""",)
		A__							:					int       =	flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		A__							:					Optional[int]       =	flax_key_tuple[:-1] + ("""weight""",)

	return flax_key_tuple, flax_tensor

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				Optional[int]					,   lowercase_:				str      )  ->   Union[str, Any]:
	if "metadata" in layer:
		A__							:					Tuple       =	layer.split("""metadata"""      )
		A__							:					Optional[Any]       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[Any]       =	[tuple(("""metadata""" + split_layer[1]).split("""/"""      )      )]
	elif "kvstore" in layer:
		A__							:					str       =	layer.split("""kvstore"""      )
		A__							:					int       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[int]       =	[tuple(("""kvstore""" + split_layer[1]).split("""/"""      )      )]

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

	if "kvstore/path" in layer:
		A__							:					Dict       =	f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A__							:					Optional[int]       =	"""file"""
	else:
		A__							:					str       =	checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				List[Any]      )  ->   int:
	A__							:					int       =	rename_keys(lowercase_      )
	A__							:					Any       =	{}
	for k, v in current_block.items():
		A__							:					Dict       =	v
	A__							:					str       =	new_current_block
	torch.save(lowercase_					,   lowercase_      )

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[int]					,   lowercase_:				str = WEIGHTS_NAME      )  ->   Tuple:
	A__							:					Optional[int]       =	convert_file_size_to_int(lowercase_      )
	A__							:					List[Any]       =	[]
	A__							:					int       =	{}
	A__							:					List[str]       =	0
	A__							:					Any       =	0

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

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

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

		# use the renaming pattern from the small conversion scripts
		A__     ,     A__							:					Any       =	rename_base_flax_keys(tuple(key.split("""/"""      )      )					,   lowercase_      )
		A__							:					Any       =	"""/""".join(lowercase_      )

		# If this weight is going to tip up over the maximal size, we split.
		if current_block_size + weight_size > max_shard_size:
			A__							:					List[Any]       =	os.path.join(
			    lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{len(lowercase_      )+1:05d}-of-???.bin"""      )      )
			rename_and_save_block(lowercase_					,   lowercase_      )
			sharded_state_dicts.append(current_block.keys()      )
			del current_block
			A__							:					Any       =	{}
			A__							:					str       =	0

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

	# Add the last block
	A__							:					Union[str, Any]       =	os.path.join(lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{len(lowercase_      )+1:05d}-of-???.bin"""      )      )
	rename_and_save_block(lowercase_					,   lowercase_      )
	sharded_state_dicts.append(current_block.keys()      )

	# If we only have one shard, we return it
	if len(lowercase_      ) == 1:
		return {weights_name: sharded_state_dicts[0]}, None

	# Otherwise, let's build the index
	A__							:					str       =	{}
	A__							:					Any       =	{}
	for idx, shard in enumerate(lowercase_      ):
		A__							:					Any       =	weights_name.replace(
		    """.bin"""					,   f"""-{idx+1:05d}-of-{len(lowercase_      ):05d}.bin"""      )  # len(sharded_state_dicts):05d}
		A__							:					Dict       =	os.path.join(lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{idx+1:05d}-of-???.bin"""      )      )
		os.rename(lowercase_					,   os.path.join(lowercase_					,   lowercase_      )      )
		A__							:					str       =	shard
		for key in shard:
			A__							:					Any       =	shard_file

    # Add the metadata
	A__							:					Tuple       =	{"""total_size""": total_size}
	A__							:					Union[str, Any]       =	{"""metadata""": metadata, """weight_map""": weight_map}

	with open(os.path.join(lowercase_					,   lowercase_      )					,   """w"""					,   encoding="""utf-8"""      ) as f:
		A__							:					Dict       =	json.dumps(lowercase_					,   indent=2					,   sort_keys=lowercase_      ) + """\n"""
		f.write(lowercase_      )

	return metadata, index


if __name__ == "__main__":
	A_ :				Optional[int]						 =    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.',
	)
	A_ :				Dict						 =    parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)


def 		UpperCamelCase      ()  ->   int:
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

	A__							:					Union[str, Any]       =	tokenizer(lowercase_					,   return_tensors="""pt"""      ).input_ids
	A__							:					Tuple       =	model.generate(lowercase_					,   decoder_start_token_id=0      )
	print(tokenizer.decode(out[0]      )      )



