



'''simple docstring'''






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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


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

       return flax_key_tuple, flax_tensor




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


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

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

       if "kvstore/path" in layer:
              _UpperCAmelCase  :						str			     = F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
       elif "kvstore/driver" in layer:
              _UpperCAmelCase  :						Any			     = """file"""
       else:
              _UpperCAmelCase  :						Optional[int]			     = checkpoint_info[layer]

       return curr_real_layer_name, split_layer, content




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		int:





       '''simple docstring'''


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = WEIGHTS_NAME		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = convert_file_size_to_int(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[Any]			     = []
       _UpperCAmelCase  :						List[str]			     = {}
       _UpperCAmelCase  :						Dict			     = 0
       _UpperCAmelCase  :						int			     = 0

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

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

       for key in all_layers.keys():
              # open tensorstore file
              _UpperCAmelCase  :						Optional[int]			     = ts.open(unflatten_dict(all_layers[key]		)		).result().read().result()
              _UpperCAmelCase  :						Dict			     = torch.tensor(lowerCAmelCase_		)
              _UpperCAmelCase  :						str			     = raw_weights.numel() * dtype_byte_size(raw_weights.dtype		)

              # use the renaming pattern from the small conversion scripts
              _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = rename_base_flax_keys(tuple(key.split("""/"""		)		)       ,   lowerCAmelCase_		)
              _UpperCAmelCase  :						Optional[int]			     = """/""".join(lowerCAmelCase_		)

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

              _UpperCAmelCase  :						str			     = raw_weights.to(getattr(lowerCAmelCase_       ,   lowerCAmelCase_		)		)
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       _UpperCAmelCase  :						Optional[Any]			     = os.path.join(lowerCAmelCase_       ,   weights_name.replace(""".bin"""       ,   F'''-{len(lowerCAmelCase_		)+1:05d}-of-???.bin'''		)		)
       rename_and_save_block(lowerCAmelCase_       ,   lowerCAmelCase_		)
       sharded_state_dicts.append(current_block.keys()		)

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

       # Otherwise, let's build the index
       _UpperCAmelCase  :						Optional[int]			     = {}
       _UpperCAmelCase  :						str			     = {}
       for idx, shard in enumerate(lowerCAmelCase_		):
              _UpperCAmelCase  :						Dict			     = weights_name.replace(
                  """.bin"""       ,   F'''-{idx+1:05d}-of-{len(lowerCAmelCase_		):05d}.bin'''		)  # len(sharded_state_dicts):05d}
              _UpperCAmelCase  :						List[str]			     = os.path.join(lowerCAmelCase_       ,   weights_name.replace(""".bin"""       ,   F'''-{idx+1:05d}-of-???.bin'''		)		)
              os.rename(lowerCAmelCase_       ,   os.path.join(lowerCAmelCase_       ,   lowerCAmelCase_		)		)
              _UpperCAmelCase  :						Optional[int]			     = shard
              for key in shard:
                     _UpperCAmelCase  :						List[str]			     = shard_file

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

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

       return metadata, index


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




def     snake_case_					(     )->		List[str]:





       '''simple docstring'''


       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

       _UpperCAmelCase  :						Union[str, Any]			     = TaTokenizer.from_pretrained("""t5-small"""		)
       _UpperCAmelCase  :						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>."""

       _UpperCAmelCase  :						int			     = tokenizer(lowerCAmelCase_       ,   return_tensors="""pt"""		).input_ids
       _UpperCAmelCase  :						List[str]			     = model.generate(lowerCAmelCase_       ,   decoder_start_token_id=0		)
       print(tokenizer.decode(out[0]		)		)








'''simple docstring'''






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
A_							: Union[str, Any]						 =	logging.get_logger(__name__)

A_							: Union[str, Any]						 =	[
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

A_							: int						 =	[
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]




def     snake_case_					(     lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)
       return sd




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=rename_keys_prefix		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = OrderedDict()
       _UpperCAmelCase  :						Any			     = torch.arange(config.max_position_embeddings		).expand((1, -1)		)
       # detector_d = OrderedDict()
       for key in d:
              if "detector" in key:
                     # detector_d[key.replace('detector.','')] = d[key]
                     continue
              _UpperCAmelCase  :						Dict			     = key
              for name_pair in rename_keys_prefix:
                     _UpperCAmelCase  :						str			     = new_key.replace(name_pair[0]       ,   name_pair[1]		)
              _UpperCAmelCase  :						str			     = d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     _UpperCAmelCase  :						int			     = new_d["""cls.predictions.bias"""]
       return new_d




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       assert (
           checkpoint_path.split("""/"""		)[-1] in ACCEPTABLE_CHECKPOINTS
       ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

       # Get Config
       if "pre" in checkpoint_path:
              _UpperCAmelCase  :						Optional[int]			     = """pretraining"""
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						Optional[int]			     = {"""visual_embedding_dim""": 512}
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						List[Any]			     = {"""visual_embedding_dim""": 2048}
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 2048}
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						Any			     = {"""visual_embedding_dim""": 1024}
              else:
                     raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''		)
       else:
              if "vcr" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 512}
                     _UpperCAmelCase  :						int			     = """multichoice"""
              elif "vqa_advanced" in checkpoint_path:
                     _UpperCAmelCase  :						str			     = {"""visual_embedding_dim""": 2048}
                     _UpperCAmelCase  :						int			     = """vqa_advanced"""
              elif "vqa" in checkpoint_path:
                     _UpperCAmelCase  :						List[str]			     = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
                     _UpperCAmelCase  :						int			     = """vqa"""
              elif "nlvr" in checkpoint_path:
                     _UpperCAmelCase  :						int			     = {
                         """visual_embedding_dim""": 1024,
                         """num_labels""": 2,
                     }
                     _UpperCAmelCase  :						Optional[Any]			     = """nlvr"""

       _UpperCAmelCase  :						int			     = VisualBertConfig(**lowerCAmelCase_		)

       # Load State Dict
       _UpperCAmelCase  :						Any			     = load_state_dict(lowerCAmelCase_		)

       _UpperCAmelCase  :						Optional[int]			     = get_new_dict(lowerCAmelCase_       ,   lowerCAmelCase_		)

       if model_type == "pretraining":
              _UpperCAmelCase  :						List[str]			     = VisualBertForPreTraining(lowerCAmelCase_		)
       elif model_type == "vqa":
              _UpperCAmelCase  :						Optional[int]			     = VisualBertForQuestionAnswering(lowerCAmelCase_		)
       elif model_type == "nlvr":
              _UpperCAmelCase  :						str			     = VisualBertForVisualReasoning(lowerCAmelCase_		)
       elif model_type == "multichoice":
              _UpperCAmelCase  :						Dict			     = VisualBertForMultipleChoice(lowerCAmelCase_		)

       model.load_state_dict(lowerCAmelCase_		)
       # Save Checkpoints
       Path(lowerCAmelCase_		).mkdir(exist_ok=lowerCAmelCase_		)
       model.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
   A_							: int						 =	parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)



