'''simple docstring'''






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def    __UpperCAmelCase			(  a_:       str,					a_:       str,					a_:       str							):
   def get_masked_lm_array(a_:       str							):
      _UpperCAmelCase							:	Any							=   f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      _UpperCAmelCase							:	List[str]							=   tf.train.load_variable(snake_case_,					snake_case_							)

      if "kernel" in name:
         _UpperCAmelCase							:	Optional[Any]							=   array.transpose()

      return torch.from_numpy(snake_case_							)

   def get_encoder_array(a_:       str							):
      _UpperCAmelCase							:	Union[str, Any]							=   f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      _UpperCAmelCase							:	int							=   tf.train.load_variable(snake_case_,					snake_case_							)

      if "kernel" in name:
         _UpperCAmelCase							:	Dict							=   array.transpose()

      return torch.from_numpy(snake_case_							)

   def get_encoder_layer_array(a_:       int,					a_:       str							):
      _UpperCAmelCase							:	Dict							=   f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      _UpperCAmelCase							:	List[Any]							=   tf.train.load_variable(snake_case_,					snake_case_							)

      if "kernel" in name:
         _UpperCAmelCase							:	Optional[int]							=   array.transpose()

      return torch.from_numpy(snake_case_							)

   def get_encoder_attention_layer_array(a_:       int,					a_:       str,					a_:       Any							):
      _UpperCAmelCase							:	int							=   f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      _UpperCAmelCase							:	Dict							=   tf.train.load_variable(snake_case_,					snake_case_							)
      _UpperCAmelCase							:	Dict							=   array.reshape(snake_case_							)

      if "kernel" in name:
         _UpperCAmelCase							:	Optional[int]							=   array.transpose()

      return torch.from_numpy(snake_case_							)

   print(f"""Loading model based on config from {config_path}..."""							)
   _UpperCAmelCase							:	List[Any]							=   BertConfig.from_json_file(snake_case_							)
   _UpperCAmelCase							:	Any							=   BertForMaskedLM(snake_case_							)

   # Layers
   for layer_index in range(0,					config.num_hidden_layers							):
      _UpperCAmelCase							:	Optional[Any]							=   model.bert.encoder.layer[layer_index]

      # Self-attention
      _UpperCAmelCase							:	Optional[Any]							=   layer.attention.self

      _UpperCAmelCase							:	List[str]							=   get_encoder_attention_layer_array(
          snake_case_,					"_query_dense/kernel",					self_attn.query.weight.data.shape							)
      _UpperCAmelCase							:	Optional[int]							=   get_encoder_attention_layer_array(
          snake_case_,					"_query_dense/bias",					self_attn.query.bias.data.shape							)
      _UpperCAmelCase							:	List[str]							=   get_encoder_attention_layer_array(
          snake_case_,					"_key_dense/kernel",					self_attn.key.weight.data.shape							)
      _UpperCAmelCase							:	List[Any]							=   get_encoder_attention_layer_array(
          snake_case_,					"_key_dense/bias",					self_attn.key.bias.data.shape							)
      _UpperCAmelCase							:	int							=   get_encoder_attention_layer_array(
          snake_case_,					"_value_dense/kernel",					self_attn.value.weight.data.shape							)
      _UpperCAmelCase							:	int							=   get_encoder_attention_layer_array(
          snake_case_,					"_value_dense/bias",					self_attn.value.bias.data.shape							)

      # Self-attention Output
      _UpperCAmelCase							:	Union[str, Any]							=   layer.attention.output

      _UpperCAmelCase							:	Dict							=   get_encoder_attention_layer_array(
          snake_case_,					"_output_dense/kernel",					self_output.dense.weight.data.shape							)
      _UpperCAmelCase							:	List[str]							=   get_encoder_attention_layer_array(
          snake_case_,					"_output_dense/bias",					self_output.dense.bias.data.shape							)

      _UpperCAmelCase							:	Optional[Any]							=   get_encoder_layer_array(snake_case_,					"_attention_layer_norm/gamma"							)
      _UpperCAmelCase							:	str							=   get_encoder_layer_array(snake_case_,					"_attention_layer_norm/beta"							)

      # Intermediate
      _UpperCAmelCase							:	Any							=   layer.intermediate

      _UpperCAmelCase							:	str							=   get_encoder_layer_array(snake_case_,					"_intermediate_dense/kernel"							)
      _UpperCAmelCase							:	Any							=   get_encoder_layer_array(snake_case_,					"_intermediate_dense/bias"							)

      # Output
      _UpperCAmelCase							:	Optional[int]							=   layer.output

      _UpperCAmelCase							:	Tuple							=   get_encoder_layer_array(snake_case_,					"_output_dense/kernel"							)
      _UpperCAmelCase							:	Any							=   get_encoder_layer_array(snake_case_,					"_output_dense/bias"							)

      _UpperCAmelCase							:	Union[str, Any]							=   get_encoder_layer_array(snake_case_,					"_output_layer_norm/gamma"							)
      _UpperCAmelCase							:	int							=   get_encoder_layer_array(snake_case_,					"_output_layer_norm/beta"							)

   # Embeddings
   _UpperCAmelCase							:	Optional[Any]							=   get_encoder_array("_position_embedding_layer/embeddings"							)
   _UpperCAmelCase							:	Optional[int]							=   get_encoder_array("_type_embedding_layer/embeddings"							)
   _UpperCAmelCase							:	Optional[int]							=   get_encoder_array("_embedding_norm_layer/gamma"							)
   _UpperCAmelCase							:	Tuple							=   get_encoder_array("_embedding_norm_layer/beta"							)

   # LM Head
   _UpperCAmelCase							:	Tuple							=   model.cls.predictions.transform

   _UpperCAmelCase							:	Optional[int]							=   get_masked_lm_array("dense/kernel"							)
   _UpperCAmelCase							:	Optional[int]							=   get_masked_lm_array("dense/bias"							)

   _UpperCAmelCase							:	int							=   get_masked_lm_array("layer_norm/gamma"							)
   _UpperCAmelCase							:	int							=   get_masked_lm_array("layer_norm/beta"							)

   _UpperCAmelCase							:	Optional[Any]							=   get_masked_lm_array("embedding_table"							)

   # Pooling
   _UpperCAmelCase							:	Optional[int]							=   BertPooler(config=snake_case_							)
   _UpperCAmelCase							:	List[str]							=   get_encoder_array("_pooler_layer/kernel"							)
   _UpperCAmelCase							:	str							=   get_encoder_array("_pooler_layer/bias"							)

   # Export final model
   model.save_pretrained(snake_case_							)

   # Integration test - should load without any errors ;)
   _UpperCAmelCase							:	Optional[Any]							=   BertForMaskedLM.from_pretrained(snake_case_							)
   print(new_model.eval()							)

   print("Model conversion was done sucessfully!"							)


if __name__ == "__main__":
     __a										=					argparse.ArgumentParser()
     parser.add_argument(
         '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
     )
     parser.add_argument(
         '--bert_config_file',
         type=str,
         required=True,
         help='The config json file corresponding to the BERT model. This specifies the model architecture.',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         type=str,
         required=True,
         help='Path to the output PyTorch model.',
     )
     __a										=					parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[str]							=    CpmAntTokenizer
      A_						:				Optional[int]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         super().setUp()

         __snake_case							       =   [
             '''<d>''',
             '''</d>''',
             '''<s>''',
             '''</s>''',
             '''</_>''',
             '''<unk>''',
             '''<pad>''',
             '''</n>''',
             '''我''',
             '''是''',
             '''C''',
             '''P''',
             '''M''',
             '''A''',
             '''n''',
             '''t''',
         ]
         __snake_case							       =   os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']      )
         with open(self.vocab_file				,			'''w'''				,			encoding='''utf-8'''      ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )


      @tooslow
      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   ['''今天''', '''天气''', '''真''', '''好''', '''！''']
         __snake_case							       =   tokenizer.tokenize(a__      )
         self.assertListEqual(a__				,			a__      )
         __snake_case							       =   '''今天天气真好！'''
         __snake_case							       =   [tokenizer.bos_token] + tokens

         __snake_case							       =   [6, 9802, 1_4962, 2082, 831, 244]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__      )				,			a__      )

         __snake_case							       =   tokenizer.decode(a__      )
         self.assertEqual(a__				,			a__      )

