





import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


snake_case_				=			logging.getLogger(__name__)

snake_case_				=			tf.data.AUTOTUNE






def lowerCamelCase__    (							)      ->				List[Any]:
   __snake_case							       =   argparse.ArgumentParser(description='''Train a masked language model on TPU.'''       )
   parser.add_argument(
       '''--pretrained_model_config'''       ,      type=__lowerCamelCase       ,      default='''roberta-base'''       ,      help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''       ,      )
   parser.add_argument(
       '''--tokenizer'''       ,      type=__lowerCamelCase       ,      default='''unigram-tokenizer-wikitext'''       ,      help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''       ,      )

   parser.add_argument(
       '''--per_replica_batch_size'''       ,      type=__lowerCamelCase       ,      default=8       ,      help='''Batch size per TPU core.'''       ,      )

   parser.add_argument(
       '''--no_tpu'''       ,      action='''store_true'''       ,      help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''       ,      )

   parser.add_argument(
       '''--tpu_name'''       ,      type=__lowerCamelCase       ,      help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''       ,      default='''local'''       ,      )

   parser.add_argument(
       '''--tpu_zone'''       ,      type=__lowerCamelCase       ,      help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''       ,      )

   parser.add_argument(
       '''--gcp_project'''       ,      type=__lowerCamelCase       ,      help='''Google cloud project name. Only used for non-Colab TPU nodes.'''       )

   parser.add_argument(
       '''--bfloat16'''       ,      action='''store_true'''       ,      help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''       ,      )

   parser.add_argument(
       '''--train_dataset'''       ,      type=__lowerCamelCase       ,      help='''Path to training dataset to load. If the path begins with `gs://`'''
       ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''       ,      )

   parser.add_argument(
       '''--shuffle_buffer_size'''       ,      type=__lowerCamelCase       ,      default=2**18       ,      help='''Size of the shuffle buffer (in samples)'''       ,      )

   parser.add_argument(
       '''--eval_dataset'''       ,      type=__lowerCamelCase       ,      help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
       ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''       ,      )

   parser.add_argument(
       '''--num_epochs'''       ,      type=__lowerCamelCase       ,      default=1       ,      help='''Number of epochs to train for.'''       ,      )

   parser.add_argument(
       '''--learning_rate'''       ,      type=__lowerCamelCase       ,      default=1e-4       ,      help='''Learning rate to use for training.'''       ,      )

   parser.add_argument(
       '''--weight_decay_rate'''       ,      type=__lowerCamelCase       ,      default=1e-3       ,      help='''Weight decay rate to use for training.'''       ,      )

   parser.add_argument(
       '''--max_length'''       ,      type=__lowerCamelCase       ,      default=512       ,      help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''       ,      )

   parser.add_argument(
       '''--mlm_probability'''       ,      type=__lowerCamelCase       ,      default=0.15       ,      help='''Fraction of tokens to mask during training.'''       ,      )

   parser.add_argument('''--output_dir'''       ,      type=__lowerCamelCase       ,      required=__lowerCamelCase       ,      help='''Path to save model checkpoints to.'''       )
   parser.add_argument('''--hub_model_id'''       ,      type=__lowerCamelCase       ,      help='''Model ID to upload to on the Hugging Face Hub.'''       )

   __snake_case							       =   parser.parse_args()
   return args






def lowerCamelCase__    (							snake_case_	:  Any       )      ->				Any:
   try:
      if args.tpu_name:
         __snake_case							       =   tf.distribute.cluster_resolver.TPUClusterResolver(
             args.tpu_name       ,      zone=args.tpu_zone       ,      project=args.gcp_project       )
      else:
         __snake_case							       =   tf.distribute.cluster_resolver.TPUClusterResolver()
   except ValueError:
      raise RuntimeError(
          '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
          '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''       )

   tf.config.experimental_connect_to_cluster(__lowerCamelCase       )
   tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase       )

   return tpu






def lowerCamelCase__    (							snake_case_	:  str       )      ->				List[str]:
   __snake_case							       =   0
   for file in file_list:
      __snake_case							       =   file.split('''/'''       )[-1]
      __snake_case							       =   re.search(R'''-\d+-(\d+)\.tfrecord'''       ,      __lowerCamelCase       ).group(1       )
      __snake_case							       =   int(__lowerCamelCase       )
      num_samples += sample_count

   return num_samples






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       ,      snake_case_	:  int       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Tuple       ,      snake_case_	:  Any=None       )      ->				Optional[int]:
   __snake_case							       =   count_samples(__lowerCamelCase       )
   __snake_case							       =   tf.data.Dataset.from_tensor_slices(__lowerCamelCase       )
   if shuffle:
      __snake_case							       =   dataset.shuffle(len(__lowerCamelCase       )       )
   __snake_case							       =   tf.data.TFRecordDataset(__lowerCamelCase       ,      num_parallel_reads=__lowerCamelCase       )
   # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
   __snake_case							       =   dataset.apply(tf.data.experimental.assert_cardinality(__lowerCamelCase       )       )
   __snake_case							       =   dataset.map(__lowerCamelCase       ,      num_parallel_calls=__lowerCamelCase       )
   if shuffle:
      assert shuffle_buffer_size is not None
      __snake_case							       =   dataset.shuffle(args.shuffle_buffer_size       )
   __snake_case							       =   dataset.batch(__lowerCamelCase       ,      drop_remainder=__lowerCamelCase       )
   __snake_case							       =   dataset.map(__lowerCamelCase       ,      num_parallel_calls=__lowerCamelCase       )
   __snake_case							       =   dataset.prefetch(__lowerCamelCase       )
   return dataset






def lowerCamelCase__    (							snake_case_	:  List[str]       )      ->				List[str]:
   if not args.no_tpu:
      __snake_case							       =   initialize_tpu(__lowerCamelCase       )
      __snake_case							       =   tf.distribute.TPUStrategy(__lowerCamelCase       )
   else:
      __snake_case							       =   tf.distribute.OneDeviceStrategy(device='''/gpu:0'''       )

   if args.bfloataa:
      tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''       )

   __snake_case							       =   AutoTokenizer.from_pretrained(args.tokenizer       )
   __snake_case							       =   AutoConfig.from_pretrained(args.pretrained_model_config       )
   __snake_case							       =   tokenizer.vocab_size

   __snake_case							       =   tf.io.gfile.glob(os.path.join(args.train_dataset       ,      '''*.tfrecord'''       )       )
   if not training_records:
      raise ValueError(f"""No .tfrecord files found in {args.train_dataset}."""       )
   __snake_case							       =   tf.io.gfile.glob(os.path.join(args.eval_dataset       ,      '''*.tfrecord'''       )       )
   if not eval_records:
      raise ValueError(f"""No .tfrecord files found in {args.eval_dataset}."""       )

   __snake_case							       =   count_samples(__lowerCamelCase       )

   __snake_case							       =   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
   __snake_case							       =   steps_per_epoch * args.num_epochs

   with strategy.scope():
      __snake_case							       =   TFAutoModelForMaskedLM.from_config(__lowerCamelCase       )
      model(model.dummy_inputs       )  # Pass some dummy inputs through the model to ensure all the weights are built
      __snake_case							       =   create_optimizer(
          num_train_steps=__lowerCamelCase       ,      num_warmup_steps=total_train_steps // 20       ,      init_lr=args.learning_rate       ,      weight_decay_rate=args.weight_decay_rate       ,      )

      # Transformers models compute the right loss for their task by default when labels are passed, and will
      # use this for training unless you specify your own loss function in compile().
      model.compile(optimizer=__lowerCamelCase       ,      metrics=['''accuracy''']       )

   def decode_fn(snake_case_	:  List[str]       ):
      __snake_case							       =   {
          "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa       ,      shape=(args.max_length,)       ),
          "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa       ,      shape=(args.max_length,)       ),
      }
      return tf.io.parse_single_example(__lowerCamelCase       ,      __lowerCamelCase       )

   # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
   # use their methods in our data pipeline.
   __snake_case							       =   DataCollatorForLanguageModeling(
       tokenizer=__lowerCamelCase       ,      mlm_probability=args.mlm_probability       ,      mlm=__lowerCamelCase       ,      return_tensors='''tf'''       )

   def mask_with_collator(snake_case_	:  List[str]       ):
      # TF really needs an isin() function
      __snake_case							       =   (
          ~tf.cast(batch['''attention_mask''']       ,      tf.bool       )
          | (batch["input_ids"] == tokenizer.cls_token_id)
          | (batch["input_ids"] == tokenizer.sep_token_id)
      )
      __snake_case							       =   data_collator.tf_mask_tokens(
          batch['''input_ids''']       ,      vocab_size=len(__lowerCamelCase       )       ,      mask_token_id=tokenizer.mask_token_id       ,      special_tokens_mask=__lowerCamelCase       ,      )
      return batch

   __snake_case							       =   args.per_replica_batch_size * strategy.num_replicas_in_sync

   __snake_case							       =   prepare_dataset(
       __lowerCamelCase       ,      decode_fn=__lowerCamelCase       ,      mask_fn=__lowerCamelCase       ,      batch_size=__lowerCamelCase       ,      shuffle=__lowerCamelCase       ,      shuffle_buffer_size=args.shuffle_buffer_size       ,      )

   __snake_case							       =   prepare_dataset(
       __lowerCamelCase       ,      decode_fn=__lowerCamelCase       ,      mask_fn=__lowerCamelCase       ,      batch_size=__lowerCamelCase       ,      shuffle=__lowerCamelCase       ,      )

   __snake_case							       =   []
   if args.hub_model_id:
      callbacks.append(
          PushToHubCallback(output_dir=args.output_dir       ,      hub_model_id=args.hub_model_id       ,      tokenizer=__lowerCamelCase       )       )

   model.fit(
       __lowerCamelCase       ,      validation_data=__lowerCamelCase       ,      epochs=args.num_epochs       ,      callbacks=__lowerCamelCase       ,      )

   model.save_pretrained(args.output_dir       )


if __name__ == "__main__":
    snake_case_				=			parse_args()
    main(args)



"""simple docstring"""





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					str			     =    checkpoint

   UpperCAmelCase_					:					int			     =    {}

   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_in.weight"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["encoder.conv_in.bias"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["encoder.conv_out.weight"]
   UpperCAmelCase_					:					Optional[int]			     =    vae_state_dict["encoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["encoder.norm_out.weight"]
   UpperCAmelCase_					:					Union[str, Any]			     =    vae_state_dict["encoder.norm_out.bias"]

   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_in.weight"]
   UpperCAmelCase_					:					int			     =    vae_state_dict["decoder.conv_in.bias"]
   UpperCAmelCase_					:					Any			     =    vae_state_dict["decoder.conv_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.conv_out.bias"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["decoder.norm_out.weight"]
   UpperCAmelCase_					:					Tuple			     =    vae_state_dict["decoder.norm_out.bias"]

   UpperCAmelCase_					:					str			     =    vae_state_dict["quant_conv.weight"]
   UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict["quant_conv.bias"]
   UpperCAmelCase_					:					List[str]			     =    vae_state_dict["post_quant_conv.weight"]
   UpperCAmelCase_					:					List[Any]			     =    vae_state_dict["post_quant_conv.bias"]

   # Retrieves the keys for the encoder down blocks only
   UpperCAmelCase_					:					Optional[Any]			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "encoder.down" in layer}  )
   UpperCAmelCase_					:					Optional[Any]			     =    {
       layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   # Retrieves the keys for the decoder up blocks only
   UpperCAmelCase_					:					Dict			     =    len({".".join(layer.split("."  )[:3]  ) for layer in vae_state_dict if "decoder.up" in layer}  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase  )
   }

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Any			     =    [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

      if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""  )
         UpperCAmelCase_					:					Dict			     =    vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""  )

      UpperCAmelCase_					:					List[str]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[Any]			     =    {"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					List[Any]			     =    [key for key in vae_state_dict if "encoder.mid.block" in key]
   UpperCAmelCase_					:					Tuple			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					List[Any]			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Optional[int]			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "encoder.mid.attn" in key]
   UpperCAmelCase_					:					Union[str, Any]			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )

   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Optional[Any]			     =    num_up_blocks - 1 - i
      UpperCAmelCase_					:					Any			     =    [
          key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
      ]

      if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
         UpperCAmelCase_					:					str			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.weight"""
         ]
         UpperCAmelCase_					:					Optional[Any]			     =    vae_state_dict[
             f"""decoder.up.{block_id}.upsample.conv.bias"""
         ]

      UpperCAmelCase_					:					Dict			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					List[str]			     =    {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[Any]			     =    [key for key in vae_state_dict if "decoder.mid.block" in key]
   UpperCAmelCase_					:					List[Any]			     =    2
   for i in range(1,		num_mid_res_blocks + 1  ):
      UpperCAmelCase_					:					str			     =    [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

      UpperCAmelCase_					:					Tuple			     =    renew_vae_resnet_paths(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )

   UpperCAmelCase_					:					Optional[int]			     =    [key for key in vae_state_dict if "decoder.mid.attn" in key]
   UpperCAmelCase_					:					Any			     =    renew_vae_attention_paths(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
   assign_to_checkpoint(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		additional_replacements=[meta_path],		config=__lowerCamelCase  )
   conv_attn_to_linear(__lowerCamelCase  )
   return new_checkpoint







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		):
   # Only support V1
   UpperCAmelCase_					:					List[str]			     =    requests.get(
       " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"  )
   UpperCAmelCase_					:					List[Any]			     =    io.BytesIO(r.content  )

   UpperCAmelCase_					:					Any			     =    OmegaConf.load(__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    512
   UpperCAmelCase_					:					Optional[Any]			     =    "cuda" if torch.cuda.is_available() else "cpu"
   if checkpoint_path.endswith("safetensors"  ):
      from safetensors import safe_open

      UpperCAmelCase_					:					int			     =    {}
      with safe_open(__lowerCamelCase,		framework="pt",		device="cpu"  ) as f:
         for key in f.keys():
            UpperCAmelCase_					:					Tuple			     =    f.get_tensor(__lowerCamelCase  )
   else:
      UpperCAmelCase_					:					Any			     =    torch.load(__lowerCamelCase,		map_location=__lowerCamelCase  )["state_dict"]

   # Convert the VAE model.
   UpperCAmelCase_					:					Dict			     =    create_vae_diffusers_config(__lowerCamelCase,		image_size=__lowerCamelCase  )
   UpperCAmelCase_					:					Union[str, Any]			     =    custom_convert_ldm_vae_checkpoint(__lowerCamelCase,		__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    AutoencoderKL(**__lowerCamelCase  )
   vae.load_state_dict(__lowerCamelCase  )
   vae.save_pretrained(__lowerCamelCase  )


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

    parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

    _a		       =							parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)


