


'''simple docstring'''
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[str]:
    UpperCAmelCase__     :						List[Any]						=							('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

    UpperCAmelCase__     :						Dict						=							(
        ('''layer.''', '''layer_'''),
        ('''word_embeddings.weight''', '''word_embeddings'''),
        ('''position_embeddings.weight''', '''position_embeddings'''),
        ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
        ('''.''', '''/'''),
        ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
        ('''LayerNorm/bias''', '''LayerNorm/beta'''),
        ('''weight''', '''kernel'''),
    )

    if not os.path.isdir(lowerCAmelCase__		):
        os.makedirs(lowerCAmelCase__		)

    UpperCAmelCase__     :						int						=							model.state_dict()

    def to_tf_var_name(lowerCAmelCase__		):
        for patt, repl in iter(lowerCAmelCase__		):
            UpperCAmelCase__     :						str						=							name.replace(lowerCAmelCase__    ,   lowerCAmelCase__		)
        return F"""bert/{name}"""

    def create_tf_var(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		):
        UpperCAmelCase__     :						Union[str, Any]						=							tf.dtypes.as_dtype(tensor.dtype		)
        UpperCAmelCase__     :						Optional[int]						=							tf.get_variable(dtype=lowerCAmelCase__    ,   shape=tensor.shape    ,   name=lowerCAmelCase__    ,   initializer=tf.zeros_initializer()		)
        session.run(tf.variables_initializer([tf_var]		)		)
        session.run(lowerCAmelCase__		)
        return tf_var

    tf.reset_default_graph()
    with tf.Session() as session:
        for var_name in state_dict:
            UpperCAmelCase__     :						Union[str, Any]						=							to_tf_var_name(lowerCAmelCase__		)
            UpperCAmelCase__     :						Union[str, Any]						=							state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose		):
                UpperCAmelCase__     :						List[Any]						=							torch_tensor.T
            UpperCAmelCase__     :						Optional[Any]						=							create_tf_var(tensor=lowerCAmelCase__    ,   name=lowerCAmelCase__    ,   session=lowerCAmelCase__		)
            tf.keras.backend.set_value(lowerCAmelCase__    ,   lowerCAmelCase__		)
            UpperCAmelCase__     :						Dict						=							session.run(lowerCAmelCase__		)
            print(F"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase__    ,   lowerCAmelCase__		)}"""		)

        UpperCAmelCase__     :						Optional[int]						=							tf.train.Saver(tf.trainable_variables()		)
        saver.save(lowerCAmelCase__    ,   os.path.join(lowerCAmelCase__    ,   model_name.replace('''-'''    ,   '''_'''		) + '''.ckpt'''		)		)





def    a__			(   lowerCAmelCase__=None		)       ->      str:
    UpperCAmelCase__     :						List[Any]						=							argparse.ArgumentParser()
    parser.add_argument('''--model_name'''    ,   type=lowerCAmelCase__    ,   required=lowerCAmelCase__    ,   help='''model name e.g. bert-base-uncased'''		)
    parser.add_argument(
        '''--cache_dir'''    ,   type=lowerCAmelCase__    ,   default=lowerCAmelCase__    ,   required=lowerCAmelCase__    ,   help='''Directory containing pytorch model'''		)
    parser.add_argument('''--pytorch_model_path'''    ,   type=lowerCAmelCase__    ,   required=lowerCAmelCase__    ,   help='''/path/to/<pytorch-model-name>.bin'''		)
    parser.add_argument('''--tf_cache_dir'''    ,   type=lowerCAmelCase__    ,   required=lowerCAmelCase__    ,   help='''Directory in which to save tensorflow model'''		)
    UpperCAmelCase__     :						str						=							parser.parse_args(lowerCAmelCase__		)

    UpperCAmelCase__     :						int						=							BertModel.from_pretrained(
        pretrained_model_name_or_path=args.model_name    ,   state_dict=torch.load(args.pytorch_model_path		)    ,   cache_dir=args.cache_dir    ,   )

    convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__    ,   ckpt_dir=args.tf_cache_dir    ,   model_name=args.model_name		)


if __name__ == "__main__":
  main()








from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


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

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

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]