import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

UpperCamelCase__											=					[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
      # loop over downblocks/upblocks

      for j in range(2):
            # loop over resnets/attentions for downblocks
            UpperCamelCase__											=					f'''down_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					f'''input_blocks.{3*i + j + 1}.0.'''
            unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

            if i < 3:
                  # no attention layers in down_blocks.3
                  UpperCamelCase__											=					f'''down_blocks.{i}.attentions.{j}.'''
                  UpperCamelCase__											=					f'''input_blocks.{3*i + j + 1}.1.'''
                  unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

      for j in range(3):
            # loop over resnets/attentions for upblocks
            UpperCamelCase__											=					f'''up_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					f'''output_blocks.{3*i + j}.0.'''
            unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

            if i > 0:
                  # no attention layers in up_blocks.0
                  UpperCamelCase__											=					f'''up_blocks.{i}.attentions.{j}.'''
                  UpperCamelCase__											=					f'''output_blocks.{3*i + j}.1.'''
                  unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

      if i < 3:
            # no downsample in down_blocks.3
            UpperCamelCase__											=					f'''down_blocks.{i}.downsamplers.0.conv.'''
            UpperCamelCase__											=					f'''input_blocks.{3*(i+1)}.0.op.'''
            unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

            # no upsample in up_blocks.3
            UpperCamelCase__											=					f'''up_blocks.{i}.upsamplers.0.'''
            UpperCamelCase__											=					f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
            unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase__											=					'mid_block.attentions.0.'
UpperCamelCase__											=					'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
      UpperCamelCase__											=					f'''mid_block.resnets.{j}.'''
      UpperCamelCase__											=					f'''middle_block.{2*j}.'''
      unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




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







      '''simple docstring'''


      UpperCAmelCase__								=  {k: k for k in unet_state_dict.keys()}
      for sd_name, hf_name in unet_conversion_map:
            UpperCAmelCase__								=  sd_name
      for k, v in mapping.items():
            if "resnets" in k:
                  for sd_part, hf_part in unet_conversion_map_resnet:
                        UpperCAmelCase__								=  v.replace(__A,  __A							)
                  UpperCAmelCase__								=  v
      for k, v in mapping.items():
            for sd_part, hf_part in unet_conversion_map_layer:
                  UpperCAmelCase__								=  v.replace(__A,  __A							)
            UpperCAmelCase__								=  v
      UpperCAmelCase__								=  {v: unet_state_dict[k] for k, v in mapping.items()}
      return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
      # down_blocks have two resnets
      for j in range(2):
            UpperCamelCase__											=					f'''encoder.down_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					f'''encoder.down.{i}.block.{j}.'''
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

      if i < 3:
            UpperCamelCase__											=					f'''down_blocks.{i}.downsamplers.0.'''
            UpperCamelCase__											=					f'''down.{i}.downsample.'''
            vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

            UpperCamelCase__											=					f'''up_blocks.{i}.upsamplers.0.'''
            UpperCamelCase__											=					f'''up.{3-i}.upsample.'''
            vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

      # up_blocks have three resnets
      # also, up blocks in hf are numbered in reverse from sd
      for j in range(3):
            UpperCamelCase__											=					f'''decoder.up_blocks.{i}.resnets.{j}.'''
            UpperCamelCase__											=					f'''decoder.up.{3-i}.block.{j}.'''
            vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
      UpperCamelCase__											=					f'''mid_block.resnets.{i}.'''
      UpperCamelCase__											=					f'''mid.block_{i+1}.'''
      vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]




def    lowerCAmelCase_ (     __A							)     ->				Dict:







      '''simple docstring'''


      return w.reshape(*w.shape,  1,  1							)




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  {k: k for k in vae_state_dict.keys()}
      for k, v in mapping.items():
            for sd_part, hf_part in vae_conversion_map:
                  UpperCAmelCase__								=  v.replace(__A,  __A							)
            UpperCAmelCase__								=  v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        UpperCAmelCase__								=  v.replace(__A,  __A							)
                  UpperCAmelCase__								=  v
      UpperCAmelCase__								=  {v: vae_state_dict[k] for k, v in mapping.items()}
      UpperCAmelCase__								=  ["q", "k", "v", "proj_out"]
      for k, v in new_state_dict.items():
            for weight_name in weights_to_convert:
                  if f"""mid.attn_1.{weight_name}.weight""" in k:
                        print(f"""Reshaping {k} for SD format"""							)
                        UpperCAmelCase__								=  reshape_weight_for_sd(__A							)
      return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCamelCase__											=					[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCamelCase__											=					{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase__											=					re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCamelCase__											=					{'q': 0, 'k': 1, 'v': 2}




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







      '''simple docstring'''


      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  {}
      UpperCAmelCase__								=  {}
      for k, v in text_enc_dict.items():
            if (
                k.endswith(".self_attn.q_proj.weight"							)
                or k.endswith(".self_attn.k_proj.weight"							)
                or k.endswith(".self_attn.v_proj.weight"							)
            ):
                  UpperCAmelCase__								=  k[: -len(".q_proj.weight"							)]
                  UpperCAmelCase__								=  k[-len("q_proj.weight"							)]
                  if k_pre not in capture_qkv_weight:
                        UpperCAmelCase__								=  [None, None, None]
                  UpperCAmelCase__								=  v
                  continue

            if (
                k.endswith(".self_attn.q_proj.bias"							)
                or k.endswith(".self_attn.k_proj.bias"							)
                or k.endswith(".self_attn.v_proj.bias"							)
            ):
                  UpperCAmelCase__								=  k[: -len(".q_proj.bias"							)]
                  UpperCAmelCase__								=  k[-len("q_proj.bias"							)]
                  if k_pre not in capture_qkv_bias:
                        UpperCAmelCase__								=  [None, None, None]
                  UpperCAmelCase__								=  v
                  continue

            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  v

      for k_pre, tensors in capture_qkv_weight.items():
            if None in tensors:
                  raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"							)
            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  torch.cat(__A							)

      for k_pre, tensors in capture_qkv_bias.items():
            if None in tensors:
                  raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"							)
            UpperCAmelCase__								=  textenc_pattern.sub(lambda __A							: protected[re.escape(m.group(0							)							)],  __A							)
            UpperCAmelCase__								=  torch.cat(__A							)

      return new_state_dict




def    lowerCAmelCase_ (     __A							)     ->				Tuple:







      '''simple docstring'''


      return text_enc_dict


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

      parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
      parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
      parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
      parser.add_argument(
          '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
      )

      UpperCamelCase__											=					parser.parse_args()

      assert args.model_path is not None, "Must provide a model path!"

      assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

      # Path for safetensors
      UpperCamelCase__											=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
      UpperCamelCase__											=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
      UpperCamelCase__											=					osp.join(args.model_path, 'text_encoder', 'model.safetensors')

      # Load models from safetensors if it exists, if it doesn't pytorch
      if osp.exists(unet_path):
            UpperCamelCase__											=					load_file(unet_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
            UpperCamelCase__											=					torch.load(unet_path, map_location='cpu')

      if osp.exists(vae_path):
            UpperCamelCase__											=					load_file(vae_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
            UpperCamelCase__											=					torch.load(vae_path, map_location='cpu')

      if osp.exists(text_enc_path):
            UpperCamelCase__											=					load_file(text_enc_path, device='cpu')
      else:
            UpperCamelCase__											=					osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
            UpperCamelCase__											=					torch.load(text_enc_path, map_location='cpu')

      # Convert the UNet model
      UpperCamelCase__											=					convert_unet_state_dict(unet_state_dict)
      UpperCamelCase__											=					{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

      # Convert the VAE model
      UpperCamelCase__											=					convert_vae_state_dict(vae_state_dict)
      UpperCamelCase__											=					{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

      # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
      UpperCamelCase__											=					'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

      if is_vaa_model:
            # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
            UpperCamelCase__											=					{'transformer.' + k: v for k, v in text_enc_dict.items()}
            UpperCamelCase__											=					convert_text_enc_state_dict_vaa(text_enc_dict)
            UpperCamelCase__											=					{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
      else:
            UpperCamelCase__											=					convert_text_enc_state_dict(text_enc_dict)
            UpperCamelCase__											=					{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

      # Put together new checkpoint
      UpperCamelCase__											=					{**unet_state_dict, **vae_state_dict, **text_enc_dict}
      if args.half:
            UpperCamelCase__											=					{k: v.half() for k, v in state_dict.items()}

      if args.use_safetensors:
            save_file(state_dict, args.checkpoint_path)
      else:
            UpperCamelCase__											=					{'state_dict': state_dict}
            torch.save(state_dict, args.checkpoint_path)




import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__											=					{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__											=					'</w>'
UpperCamelCase__											=					'@@ '




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  set()
      UpperCAmelCase__								=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            UpperCAmelCase__								=  char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase__											=					{'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			['input_ids', 'attention_mask']


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Dict="<s>"       ,  __UpperCAmelCase     :						Tuple="<pad>"       ,  __UpperCAmelCase     :						str="</s>"       ,  __UpperCAmelCase     :						int="<unk>"       ,  __UpperCAmelCase     :						List[str]=False       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             unk_token=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  do_lower_case

         with open(__UpperCAmelCase       ,  encoding="utf-8"		) as vocab_handle:
               UpperCAmelCase__								=  json.load(__UpperCAmelCase		)
         UpperCAmelCase__								=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""		)

               UpperCAmelCase__								=  None
               UpperCAmelCase__								=  None
         else:
               with open(__UpperCAmelCase       ,  encoding="utf-8"		) as merges_handle:
                     UpperCAmelCase__								=  merges_handle.read().split("\n"		)[:-1]

               UpperCAmelCase__								=  [tuple(merge.split()[:2]		) for merge in merges]
               UpperCAmelCase__								=  dict(zip(__UpperCAmelCase       ,  range(len(__UpperCAmelCase		)		)		)		)
               UpperCAmelCase__								=  {}


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return len(self.decoder		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         return dict(self.encoder       ,  **self.added_tokens_encoder		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)

         if not pairs:
               return token

         while True:
               UpperCAmelCase__								=  min(__UpperCAmelCase       ,  key=lambda __UpperCAmelCase		: self.bpe_ranks.get(__UpperCAmelCase       ,  float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                     break
               UpperCAmelCase__		,      UpperCAmelCase__								=  bigram
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  0
               while i < len(__UpperCAmelCase		):
                     try:
                           UpperCAmelCase__								=  word.index(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     except ValueError:
                           new_word.extend(word[i:]		)
                           break
                     else:
                           new_word.extend(word[i:j]		)
                           UpperCAmelCase__								=  j

                     if word[i] == first and i < len(__UpperCAmelCase		) - 1 and word[i + 1] == second:
                           new_word.append(first + second		)
                           i += 2
                     else:
                           new_word.append(word[i]		)
                           i += 1
               UpperCAmelCase__								=  tuple(__UpperCAmelCase		)
               UpperCAmelCase__								=  new_word
               if len(__UpperCAmelCase		) == 1:
                     break
               else:
                     UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         if word == "\n  " + BPE_TOKEN_MERGES:
               UpperCAmelCase__								=  "\n" + BPE_TOKEN_MERGES

         if word.endswith(__UpperCAmelCase		):
               UpperCAmelCase__								=  word.replace(__UpperCAmelCase       ,  ""		)

         UpperCAmelCase__								=  word.replace(" "       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  word
         return word


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """simple docstring"""



         if self.bpe_ranks is None:
               raise ValueError(
                   "This tokenizer was instantiated without a `merges.txt` file, so"
                   " that it can only be used for decoding, not for encoding."
                   "Make sure to provide `merges.txt` file at instantiation to enable "
                   "encoding."		)

         if self.do_lower_case:
               UpperCAmelCase__								=  text.lower()

         UpperCAmelCase__								=  text.split()

         UpperCAmelCase__								=  []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase		).split(" "		)		)		)

         return split_tokens


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



         return self.encoder.get(__UpperCAmelCase       ,  self.encoder.get(self.unk_token		)		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.decoder.get(__UpperCAmelCase       ,  self.unk_token		)
         return result


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)

         # make sure @@ tokens are concatenated
         UpperCAmelCase__								=  "".join(string.split(__UpperCAmelCase		)		)

         return string





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=__UpperCAmelCase       ,  ensure_ascii=__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda __UpperCAmelCase		: kv[1]		):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               " Please check that the tokenizer is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(" ".join(__UpperCAmelCase		) + "\n"		)
                     index += 1

         return (vocab_file, merges_file)



