






import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

__snake_case												=  {
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

__snake_case												=  {
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}


def   _A   (				SCREAMING_SNAKE_CASE__  :	str       ):
    UpperCamelCase       :Dict								=   EfficientNetConfig()
    UpperCamelCase       :Optional[int]								=   CONFIG_MAP[model_name]['''hidden_dim''']
    UpperCamelCase       :Optional[Any]								=   CONFIG_MAP[model_name]['''width_coef''']
    UpperCamelCase       :Tuple								=   CONFIG_MAP[model_name]['''depth_coef''']
    UpperCamelCase       :Optional[Any]								=   CONFIG_MAP[model_name]['''image_size''']
    UpperCamelCase       :str								=   CONFIG_MAP[model_name]['''dropout_rate''']
    UpperCamelCase       :Optional[Any]								=   CONFIG_MAP[model_name]['''dw_padding''']

    UpperCamelCase       :Dict								=   '''huggingface/label-files'''
    UpperCamelCase       :Optional[int]								=   '''imagenet-1k-id2label.json'''
    UpperCamelCase       :List[str]								=   1000
    UpperCamelCase       :List[Any]								=   json.load(open(hf_hub_download(__UpperCAmelCase     ,  __UpperCAmelCase     ,  repo_type='''dataset'''       )     ,  '''r'''       )       )
    UpperCamelCase       :Optional[int]								=   {int(__UpperCAmelCase       ): v for k, v in idalabel.items()}

    UpperCamelCase       :List[Any]								=   idalabel
    UpperCamelCase       :List[Any]								=   {v: k for k, v in idalabel.items()}
    return config


def   _A   (				):
    UpperCamelCase       :Union[str, Any]								=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase       :Optional[Any]								=   Image.open(requests.get(__UpperCAmelCase     ,  stream=__UpperCAmelCase       ).raw       )
    return im


def   _A   (				SCREAMING_SNAKE_CASE__  :	Tuple       ):
    UpperCamelCase       :Dict								=   CONFIG_MAP[model_name]['''image_size''']
    UpperCamelCase       :Optional[Any]								=   EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}     ,  image_mean=[0.4_85, 0.4_56, 0.4_06]     ,  image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]     ,  do_center_crop=__UpperCAmelCase     ,  )
    return preprocessor


def   _A   (				SCREAMING_SNAKE_CASE__  :	int       ):
    UpperCamelCase       :Tuple								=   [v.split('''_'''       )[0].split('''block'''       )[1] for v in original_param_names if v.startswith('''block'''       )]
    UpperCamelCase       :List[str]								=   sorted(set(__UpperCAmelCase       )       )
    UpperCamelCase       :int								=   len(__UpperCAmelCase       )
    UpperCamelCase       :Union[str, Any]								=   {b: str(__UpperCAmelCase       ) for b, i in zip(__UpperCAmelCase     ,  range(__UpperCAmelCase       )       )}

    UpperCamelCase       :int								=   []
    rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')       )
    rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')       )
    rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')       )
    rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')       )
    rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')       )

    for b in block_names:
        UpperCamelCase       :str								=   block_name_mapping[b]
        rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')       )
        rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')       )
        rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')       )
        rename_keys.append(
            (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')       )
        rename_keys.append(
            (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')       )
        rename_keys.append(
            (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')       )
        rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')       )
        rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')       )
        rename_keys.append(
            (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')       )
        rename_keys.append(
            (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')       )

        rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')       )
        rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')       )
        rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')       )
        rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')       )
        rename_keys.append(
            (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')       )
        rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')       )
        rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')       )
        rename_keys.append(
            (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')       )
        rename_keys.append(
            (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')       )

    rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')       )
    rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')       )
    rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')       )
    rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')       )
    rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')       )

    UpperCamelCase       :Optional[int]								=   {}
    for item in rename_keys:
        if item[0] in original_param_names:
            UpperCamelCase       :List[Any]								=   '''efficientnet.''' + item[1]

    UpperCamelCase       :Dict								=   '''classifier.weight'''
    UpperCamelCase       :List[Any]								=   '''classifier.bias'''
    return key_mapping


def   _A   (				SCREAMING_SNAKE_CASE__  :	str     ,  SCREAMING_SNAKE_CASE__  :	int     ,  SCREAMING_SNAKE_CASE__  :	str       ):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        UpperCamelCase       :Optional[int]								=   key_mapping[key]
        if "_conv" in key and "kernel" in key:
            UpperCamelCase       :int								=   torch.from_numpy(__UpperCAmelCase       ).permute(3     ,  2     ,  0     ,  1       )
        elif "depthwise_kernel" in key:
            UpperCamelCase       :str								=   torch.from_numpy(__UpperCAmelCase       ).permute(2     ,  3     ,  0     ,  1       )
        elif "kernel" in key:
            UpperCamelCase       :Tuple								=   torch.from_numpy(np.transpose(__UpperCAmelCase       )       )
        else:
            UpperCamelCase       :Tuple								=   torch.from_numpy(__UpperCAmelCase       )

        # Replace HF parameters with original TF model parameters
        assert hf_params[hf_key].shape == new_hf_value.shape
        hf_params[hf_key].copy_(__UpperCAmelCase       )


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	Any     ,  SCREAMING_SNAKE_CASE__  :	List[str]     ,  SCREAMING_SNAKE_CASE__  :	Tuple     ,  SCREAMING_SNAKE_CASE__  :	int       ):
    UpperCamelCase       :List[Any]								=   model_classes[model_name](
        include_top=__UpperCAmelCase     ,  weights='''imagenet'''     ,  input_tensor=__UpperCAmelCase     ,  input_shape=__UpperCAmelCase     ,  pooling=__UpperCAmelCase     ,  classes=1000     ,  classifier_activation='''softmax'''     ,  )

    UpperCamelCase       :str								=   original_model.trainable_variables
    UpperCamelCase       :Union[str, Any]								=   original_model.non_trainable_variables
    UpperCamelCase       :Union[str, Any]								=   {param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        UpperCamelCase       :Tuple								=   param.numpy()
    UpperCamelCase       :Dict								=   list(tf_params.keys()       )

    # Load HuggingFace model
    UpperCamelCase       :Dict								=   get_efficientnet_config(__UpperCAmelCase       )
    UpperCamelCase       :List[Any]								=   EfficientNetForImageClassification(__UpperCAmelCase       ).eval()
    UpperCamelCase       :Union[str, Any]								=   hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''       )
    UpperCamelCase       :List[str]								=   rename_keys(__UpperCAmelCase       )
    replace_params(__UpperCAmelCase     ,  __UpperCAmelCase     ,  __UpperCAmelCase       )

    # Initialize preprocessor and preprocess input image
    UpperCamelCase       :Dict								=   convert_image_processor(__UpperCAmelCase       )
    UpperCamelCase       :Dict								=   preprocessor(images=prepare_img()     ,  return_tensors='''pt'''       )

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        UpperCamelCase       :Tuple								=   hf_model(**__UpperCAmelCase       )
    UpperCamelCase       :List[str]								=   outputs.logits.detach().numpy()

    # Original model inference
    UpperCamelCase       :int								=   False
    UpperCamelCase       :Dict								=   CONFIG_MAP[model_name]['''image_size''']
    UpperCamelCase       :Optional[int]								=   prepare_img().resize((image_size, image_size)     ,  resample=PIL.Image.NEAREST       )
    UpperCamelCase       :str								=   image.img_to_array(__UpperCAmelCase       )
    UpperCamelCase       :Any								=   np.expand_dims(__UpperCAmelCase     ,  axis=0       )
    UpperCamelCase       :str								=   original_model.predict(__UpperCAmelCase       )

    # Check whether original and HF model outputs match  -> np.allclose
    assert np.allclose(__UpperCAmelCase     ,  __UpperCAmelCase     ,  atol=1e-3       ), "The predicted logits are not the same."
    print('''Model outputs match!'''       )

    if save_model:
        # Create folder to save model
        if not os.path.isdir(__UpperCAmelCase       ):
            os.mkdir(__UpperCAmelCase       )
        # Save converted model and image processor
        hf_model.save_pretrained(__UpperCAmelCase       )
        preprocessor.save_pretrained(__UpperCAmelCase       )

    if push_to_hub:
        # Push model and image processor to hub
        print(F'''Pushing converted {model_name} to the hub...'''       )
        UpperCamelCase       :Optional[Any]								=   F'''efficientnet-{model_name}'''
        preprocessor.push_to_hub(__UpperCAmelCase       )
        hf_model.push_to_hub(__UpperCAmelCase       )


if __name__ == "__main__":
 __snake_case												=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""b0""",
     type=str,
     help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""hf_model""",
     type=str,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

 __snake_case												=  parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)











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

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


lowerCamelCase__ :   List[Any]					=							logging.get_logger(__name__)


lowerCamelCase__ :   List[str]					=							{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__ :   Union[str, Any]					=							{
    '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'
        ),
    },
}

lowerCamelCase__ :   Optional[Any]					=							'</w>'
lowerCamelCase__ :   Union[str, Any]					=							'@@ '

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			set()
      SCREAMING_SNAKE_CASE_							=			word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            SCREAMING_SNAKE_CASE_							=			char
      return pairs


# Speech2Text2 has no max input length
lowerCamelCase__ :   Any					=							{'facebook/s2t-wav2vec2-large-en-de': 1_024}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]



 def __init__(							self  :		List[str]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]="<s>"				,				_lowerCAmelCase  :		Any="<pad>"				,				_lowerCAmelCase  :		List[str]="</s>"				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Optional[Any]=False				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		Tuple				,				):
       super().__init__(
           unk_token=_lowerCAmelCase				,				bos_token=_lowerCAmelCase				,				eos_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			do_lower_case

       with open(_lowerCAmelCase				,				encoding='utf-8'						) as vocab_handle:
             SCREAMING_SNAKE_CASE_							=			json.load(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{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."						)

             SCREAMING_SNAKE_CASE_							=			None
             SCREAMING_SNAKE_CASE_							=			None
       else:
             with open(_lowerCAmelCase				,				encoding='utf-8'						) as merges_handle:
                   SCREAMING_SNAKE_CASE_							=			merges_handle.read().split('\n'						)[:-1]

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



 @property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return len(self.decoder						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return dict(self.encoder				,				**self.added_tokens_encoder						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
             return self.cache[token]
       SCREAMING_SNAKE_CASE_							=			get_pairs(_lowerCAmelCase						)

       if not pairs:
             return token

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

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

       if word.endswith(_lowerCAmelCase						):
             SCREAMING_SNAKE_CASE_							=			word.replace(_lowerCAmelCase				,				''						)

       SCREAMING_SNAKE_CASE_							=			word.replace(' '				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			word
       return word



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[int]						):

       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:
             SCREAMING_SNAKE_CASE_							=			text.lower()

       SCREAMING_SNAKE_CASE_							=			text.split()

       SCREAMING_SNAKE_CASE_							=			[]
       for token in text:
             if token:
                   split_tokens.extend(list(self.bpe(_lowerCAmelCase						).split(' '						)						)						)

       return split_tokens



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str						):
       return self.encoder.get(_lowerCAmelCase				,				self.encoder.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.decoder.get(_lowerCAmelCase				,				self.unk_token						)
       return result



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			' '.join(_lowerCAmelCase						)

       # make sure @@ tokens are concatenated
       SCREAMING_SNAKE_CASE_							=			''.join(string.split(_lowerCAmelCase						)						)

       return string




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       if not os.path.isdir(_lowerCAmelCase						):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
             return
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
       SCREAMING_SNAKE_CASE_							=			os.path.join(
           _lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

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

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

       with open(_lowerCAmelCase				,				'w'				,				encoding='utf-8'						) as writer:
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,				key=lambda _lowerCAmelCase						: 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!'						)
                         SCREAMING_SNAKE_CASE_							=			token_index
                   writer.write(' '.join(_lowerCAmelCase						) + '\n'						)
                   index += 1

       return (vocab_file, merges_file)