import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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







def  __UpperCamelCase    (						A							):
     # initialize config
     if "resnet-50" in model_name:
          UpperCamelCase__										=      ResNetConfig.from_pretrained('''microsoft/resnet-50'''							)
     elif "resnet-101" in model_name:
          UpperCamelCase__										=      ResNetConfig.from_pretrained('''microsoft/resnet-101'''							)
     else:
          raise ValueError('''Model name should include either resnet50 or resnet101'''							)

     UpperCamelCase__										=      DetrConfig(use_timm_backbone=A ,							backbone_config=A							)

     # set label attributes
     UpperCamelCase__										=      '''panoptic''' in model_name
     if is_panoptic:
          UpperCamelCase__										=      250
     else:
          UpperCamelCase__										=      91
          UpperCamelCase__										=      '''huggingface/label-files'''
          UpperCamelCase__										=      '''coco-detection-id2label.json'''
          UpperCamelCase__										=      json.load(open(hf_hub_download(A ,							A ,							repo_type='''dataset'''							) ,							'''r'''							)							)
          UpperCamelCase__										=      {int(A							): v for k, v in idalabel.items()}
          UpperCamelCase__										=      idalabel
          UpperCamelCase__										=      {v: k for k, v in idalabel.items()}

     return config, is_panoptic







def  __UpperCamelCase    (						A							):
     # here we list all keys to be renamed (original name on the left, our name on the right)
     UpperCamelCase__										=      []

     # stem
     # fmt: off
     rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')							)
     rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')							)
     rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')							)
     rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')							)
     rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')							)
     # stages
     for stage_idx in range(len(config.backbone_config.depths							)							):
          for layer_idx in range(config.backbone_config.depths[stage_idx]							):
               # shortcut
               if layer_idx == 0:
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                        )							)
               # 3 convs
               for i in range(3							):
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                        )							)
                    rename_keys.append(
                        (
                            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                        )							)
    # fmt: on

     for i in range(config.encoder_layers							):
          # 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}.multihead_attn.out_proj.weight",
                  f"decoder.layers.{i}.encoder_attn.out_proj.weight",
              )							)
          rename_keys.append(
              (
                  f"transformer.decoder.layers.{i}.multihead_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")							)

     # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
     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'''),
         ]							)

     return rename_keys







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      state_dict.pop(A							)
     UpperCamelCase__										=      val







def  __UpperCamelCase    (						A ,							A=False							):
     UpperCamelCase__										=      ''''''
     if is_panoptic:
          UpperCamelCase__										=      '''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)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"							)
          UpperCamelCase__										=      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
          UpperCamelCase__										=      in_proj_weight[:256, :]
          UpperCamelCase__										=      in_proj_bias[:256]
          UpperCamelCase__										=      in_proj_weight[256:512, :]
          UpperCamelCase__										=      in_proj_bias[256:512]
          UpperCamelCase__										=      in_proj_weight[-256:, :]
          UpperCamelCase__										=      in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6							):
          # read in weights + bias of input projection layer of self-attention
          UpperCamelCase__										=      state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"							)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"							)
          # next, add query, keys and values (in that order) to the state dict
          UpperCamelCase__										=      in_proj_weight[:256, :]
          UpperCamelCase__										=      in_proj_bias[:256]
          UpperCamelCase__										=      in_proj_weight[256:512, :]
          UpperCamelCase__										=      in_proj_bias[256:512]
          UpperCamelCase__										=      in_proj_weight[-256:, :]
          UpperCamelCase__										=      in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          UpperCamelCase__										=      state_dict.pop(
              f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"							)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"							)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          UpperCamelCase__										=      in_proj_weight_cross_attn[:256, :]
          UpperCamelCase__										=      in_proj_bias_cross_attn[:256]
          UpperCamelCase__										=      in_proj_weight_cross_attn[256:512, :]
          UpperCamelCase__										=      in_proj_bias_cross_attn[256:512]
          UpperCamelCase__										=      in_proj_weight_cross_attn[-256:, :]
          UpperCamelCase__										=      in_proj_bias_cross_attn[-256:]







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A							).raw							)

     return im







@torch.no_grad()
def  __UpperCamelCase    (						A ,							A=None ,							A=False							):
     UpperCamelCase__						,       UpperCamelCase__										=      get_detr_config(A							)

     # load original model from torch hub
     UpperCamelCase__										=      {
         '''detr-resnet-50''': '''detr_resnet50''',
         '''detr-resnet-101''': '''detr_resnet101''',
     }
     logger.info(f"Converting model {model_name}..."							)
     UpperCamelCase__										=      torch.hub.load('''facebookresearch/detr''' ,							model_name_to_original_name[model_name] ,							pretrained=A							).eval()
     UpperCamelCase__										=      detr.state_dict()
     # rename keys
     for src, dest in create_rename_keys(A							):
          if is_panoptic:
               UpperCamelCase__										=      '''detr.''' + src
          rename_key(A ,							A ,							A							)
     # query, key and value matrices need special treatment
     read_in_q_k_v(A ,							is_panoptic=A							)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCamelCase__										=      '''detr.model.''' if is_panoptic else '''model.'''
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith('''detr'''							)
                   and not key.startswith('''class_labels_classifier'''							)
                   and not key.startswith('''bbox_predictor'''							)
               ):
                    UpperCamelCase__										=      state_dict.pop(A							)
                    UpperCamelCase__										=      val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    UpperCamelCase__										=      state_dict.pop(A							)
                    UpperCamelCase__										=      val
               elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
                    continue
               else:
                    UpperCamelCase__										=      state_dict.pop(A							)
                    UpperCamelCase__										=      val
          else:
               if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
                    UpperCamelCase__										=      state_dict.pop(A							)
                    UpperCamelCase__										=      val

    # finally, create HuggingFace model and load state dict
     UpperCamelCase__										=      DetrForSegmentation(A							) if is_panoptic else DetrForObjectDetection(A							)
     model.load_state_dict(A							)
     model.eval()

     # verify our conversion on an image
     UpperCamelCase__										=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
     UpperCamelCase__										=      DetrImageProcessor(format=A							)

     UpperCamelCase__										=      processor(images=prepare_img() ,							return_tensors='''pt'''							)
     UpperCamelCase__										=      encoding['''pixel_values''']

     UpperCamelCase__										=      detr(A							)
     UpperCamelCase__										=      model(A							)

     assert torch.allclose(outputs.logits ,							original_outputs['''pred_logits'''] ,							atol=1e-3							)
     assert torch.allclose(outputs.pred_boxes ,							original_outputs['''pred_boxes'''] ,							atol=1e-3							)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks ,							original_outputs['''pred_masks'''] ,							atol=1e-4							)
     print('''Looks ok!'''							)

     if pytorch_dump_folder_path is not None:
          # Save model and image processor
          logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."							)
          Path(A							).mkdir(exist_ok=A							)
          model.save_pretrained(A							)
          processor.save_pretrained(A							)

     if push_to_hub:
          # Upload model and image processor to the hub
          logger.info('''Uploading PyTorch model and image processor to the hub...'''							)
          model.push_to_hub(f"nielsr/{model_name}"							)
          processor.push_to_hub(f"nielsr/{model_name}"							)


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()

     parser.add_argument(
         '''--model_name''',
         default='''detr-resnet-50''',
         type=str,
         choices=['''detr-resnet-50''', '''detr-resnet-101'''],
         help='''Name of the 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.'''
     )
     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
     __magic_name__								=parser.parse_args()
     convert_detr_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  __UpperCamelCase    (						A ,							A							):
     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      torch.permute(A ,							(0, 2, 1)							)
     elif flax_key_tuple[-1] == "kernel" and ".".join(A							):
          # linear layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor







def  __UpperCamelCase    (						A ,							A ,							A							):
     if "metadata" in layer:
          UpperCamelCase__										=      layer.split('''metadata'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''metadata''' + split_layer[1]).split('''/'''							)							)]
     elif "kvstore" in layer:
          UpperCamelCase__										=      layer.split('''kvstore'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''kvstore''' + split_layer[1]).split('''/'''							)							)]

     else:
          UpperCamelCase__										=      layer.split('''/'''							)
          UpperCamelCase__										=      '''/'''.join(split_layer[:-1]							)
          UpperCamelCase__										=      (split_layer[-1],)

     if "kvstore/path" in layer:
          UpperCamelCase__										=      f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
     elif "kvstore/driver" in layer:
          UpperCamelCase__										=      '''file'''
     else:
          UpperCamelCase__										=      checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      rename_keys(A							)
     UpperCamelCase__										=      {}
     for k, v in current_block.items():
          UpperCamelCase__										=      v
     UpperCamelCase__										=      new_current_block
     torch.save(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A = WEIGHTS_NAME							):
     UpperCamelCase__										=      convert_file_size_to_int(A							)
     UpperCamelCase__										=      []
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

     os.makedirs(A ,							exist_ok=A							)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' ,							'''rb'''							) as fp:
          UpperCamelCase__										=      serialization.msgpack_restore(fp.read()							)['''optimizer''']['''target''']
          UpperCamelCase__										=      flatten_dict(A ,							sep='''/'''							)

     UpperCamelCase__										=      {}
     for layer in checkpoint_info.keys():
          UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      get_key_and_tensorstore_dict(
              A ,							A ,							A							)
          if curr_real_layer_name in all_layers:
               UpperCamelCase__										=      content
          else:
               UpperCamelCase__										=      {split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          UpperCamelCase__						,       UpperCamelCase__										=      rename_base_flax_keys(tuple(key.split('''/'''							)							) ,							A							)
          UpperCamelCase__										=      '''/'''.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:
               UpperCamelCase__										=      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
               UpperCamelCase__										=      {}
               UpperCamelCase__										=      0

          UpperCamelCase__										=      raw_weights.to(getattr(A ,							A							)							)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     UpperCamelCase__										=      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
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      {}
     for idx, shard in enumerate(A							):
          UpperCamelCase__										=      weights_name.replace(
              '''.bin''' ,							f"-{idx+1:05d}-of-{len(A							):05d}.bin"							)  # len(sharded_state_dicts):05d}
          UpperCamelCase__										=      os.path.join(A ,							weights_name.replace('''.bin''' ,							f"-{idx+1:05d}-of-???.bin"							)							)
          os.rename(A ,							os.path.join(A ,							A							)							)
          UpperCamelCase__										=      shard
          for key in shard:
               UpperCamelCase__										=      shard_file

    # Add the metadata
     UpperCamelCase__										=      {'''total_size''': total_size}
     UpperCamelCase__										=      {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(A ,							A							) ,							'''w''' ,							encoding='''utf-8'''							) as f:
          UpperCamelCase__										=      json.dumps(A ,							indent=2 ,							sort_keys=A							) + '''\n'''
          f.write(A							)

     return metadata, index


if __name__ == "__main__":
     __magic_name__								=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.''',
     )
     __magic_name__								=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    (						):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     UpperCamelCase__										=      SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''							)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''							)
     UpperCamelCase__										=      SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted''' ,							device_map='''auto'''							)

     UpperCamelCase__										=      TaTokenizer.from_pretrained('''t5-small'''							)
     UpperCamelCase__										=      '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     UpperCamelCase__										=      tokenizer(A ,							return_tensors='''pt'''							).input_ids
     UpperCamelCase__										=      model.generate(A ,							decoder_start_token_id=0							)
     print(tokenizer.decode(out[0]							)							)



