




'''simple docstring'''







import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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





def 		snake_case_							(_a			:							Any					,     _a			:							int					,     _a			:							str  ):
    UpperCAmelCase     =	os.path.abspath(_snake_case  )
    logger.info(F"Converting TensorFlow checkpoint from {tf_path}"  )
    # Load weights from TF model
    UpperCAmelCase     =	tf.train.list_variables(_snake_case  )
    UpperCAmelCase     =	[]
    UpperCAmelCase     =	[]
    UpperCAmelCase     =	[]
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        UpperCAmelCase     =	full_name.split('''/'''  )
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(F"Skipping non-model layer {full_name}"  )
            continue
        if "optimizer" in full_name:
            logger.info(F"Skipping optimization layer {full_name}"  )
            continue
        if name[0] == "model":
            # ignore initial 'model'
            UpperCAmelCase     =	name[1:]
        # figure out how many levels deep the name is
        UpperCAmelCase     =	0
        for _name in name:
            if _name.startswith('''layer_with_weights'''  ):
                depth += 1
            else:
                break
        layer_depth.append(_snake_case  )
        # read data
        UpperCAmelCase     =	tf.train.load_variable(_snake_case					,     _snake_case  )
        names.append('''/'''.join(_snake_case  )  )
        arrays.append(_snake_case  )
    logger.info(F"Read a total of {len(_snake_case  ):,} layers"  )

    # Sanity check
    if len(set(_snake_case  )  ) != 1:
        raise ValueError(F"Found layer names with different depths (layer depth {list(set(_snake_case  )  )})"  )
    UpperCAmelCase     =	list(set(_snake_case  )  )[0]
    if layer_depth != 1:
        raise ValueError(
            '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
            ''' heads.'''  )

    # convert layers
    logger.info('''Converting weights...'''  )
    for full_name, array in zip(_snake_case					,     _snake_case  ):
        UpperCAmelCase     =	full_name.split('''/'''  )
        UpperCAmelCase     =	model
        UpperCAmelCase     =	[]
        for i, m_name in enumerate(_snake_case  ):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights'''  ):
                UpperCAmelCase     =	int(m_name.split('''-'''  )[-1]  )
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['''embeddings''', '''LayerNorm''']  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''embeddings'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''LayerNorm'''  )
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4  )]  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''encoder'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''layer'''  )
                    UpperCAmelCase     =	pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense''']  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''pooler'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''dense'''  )
            elif m_name == "embeddings":
                trace.append('''embeddings'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''embeddings'''  )
                if layer_num == 0:
                    trace.append('''word_embeddings'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''word_embeddings'''  )
                elif layer_num == 1:
                    trace.append('''position_embeddings'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''position_embeddings'''  )
                elif layer_num == 2:
                    trace.append('''token_type_embeddings'''  )
                    UpperCAmelCase     =	getattr(_snake_case					,     '''token_type_embeddings'''  )
                else:
                    raise ValueError(F"Unknown embedding layer with name {full_name}"  )
                trace.append('''weight'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''weight'''  )
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''attention'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''self'''  )
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''attention'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''output'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''LayerNorm'''  )
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''attention'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''output'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''dense'''  )
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''output'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''dense'''  )
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''output'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''LayerNorm'''  )
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''key'''  )
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''query'''  )
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''value'''  )
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense''']  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''intermediate'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''dense'''  )
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''output'''  )
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''bias'''  )
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight'''  )
                UpperCAmelCase     =	getattr(_snake_case					,     '''weight'''  )
            else:
                logger.warning(F"Ignored {m_name}"  )
        # for certain layers reshape is necessary
        UpperCAmelCase     =	'''.'''.join(_snake_case  )
        if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''					,     _snake_case  ) or re.match(
            R'''(\S+)\.attention\.output\.dense\.weight'''					,     _snake_case  ):
            UpperCAmelCase     =	array.reshape(pointer.data.shape  )
        if "kernel" in full_name:
            UpperCAmelCase     =	array.transpose()
        if pointer.shape == array.shape:
            UpperCAmelCase     =	torch.from_numpy(_snake_case  )
        else:
            raise ValueError(
                F"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"
                F" {array.shape}"  )
        logger.info(F"Successfully set variable {full_name} to PyTorch layer {trace}"  )
    return model





def 		snake_case_							(_a			:							Any					,     _a			:							List[Any]					,     _a			:							str  ):
    # Instantiate model
    logger.info(F"Loading model based on config from {config_path}..."  )
    UpperCAmelCase     =	BertConfig.from_json_file(_snake_case  )
    UpperCAmelCase     =	BertModel(_snake_case  )

    # Load weights from checkpoint
    logger.info(F"Loading weights from checkpoint {tf_checkpoint_path}..."  )
    load_tfa_weights_in_bert(_snake_case					,     _snake_case					,     _snake_case  )

    # Save pytorch-model
    logger.info(F"Saving PyTorch model to {pytorch_dump_path}..."  )
    torch.save(model.state_dict()					,     _snake_case  )


if __name__ == "__main__":
   A									=argparse.ArgumentParser()
   parser.add_argument(
       '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x 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 (must include filename).',
   )
   A									=parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      MgpstrTokenizer
       __a       :  Optional[Any]            =      False
       __a       :  str            =      {}
       __a       :  Optional[int]            =      False
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().setUp()

           # fmt: off
           UpperCAmelCase     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
           # fmt: on
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write(json.dumps(lowercase       ) + '''\n'''       )
       def 					A   (			self		:			int     ,       **lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			int     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''tester'''
           UpperCAmelCase     =	'''tester'''
           return input_text, output_text
       @unittest.skip('''MGP-STR always lower cases letters.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers(do_lower_case=lowercase       )
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase     =	'''[SPECIAL_TOKEN]'''

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   UpperCAmelCase     =	tokenizer.encode([special_token]     ,       add_special_tokens=lowercase       )
                   self.assertEqual(len(lowercase       )     ,       1       )

                   UpperCAmelCase     =	tokenizer.decode(lowercase     ,       skip_special_tokens=lowercase       )
                   self.assertTrue(special_token not in decoded       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizers()
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"       ):
                   UpperCAmelCase ,  UpperCAmelCase     =	self.get_input_output_texts(lowercase       )

                   UpperCAmelCase     =	tokenizer.tokenize(lowercase       )
                   UpperCAmelCase     =	tokenizer.convert_tokens_to_ids(lowercase       )
                   UpperCAmelCase     =	tokenizer.encode(lowercase     ,       add_special_tokens=lowercase       )
                   self.assertListEqual(lowercase     ,       lowercase       )

                   UpperCAmelCase     =	tokenizer.convert_ids_to_tokens(lowercase       )
                   self.assertNotEqual(len(lowercase       )     ,       0       )
                   UpperCAmelCase     =	tokenizer.decode(lowercase       )
                   self.assertIsInstance(lowercase     ,       lowercase       )

                   self.assertEqual(text_a.replace(''' '''     ,       ''''''       )     ,       lowercase       )
       @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass







       @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           pass




