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 lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    def get_masked_lm_array(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:Dict        =		f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __UpperCamelCase					:Dict        =		tf.train.load_variable(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

        return torch.from_numpy(SCREAMING_SNAKE_CASE     )

    def get_encoder_array(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:Tuple        =		f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __UpperCamelCase					:Any        =		tf.train.load_variable(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

        return torch.from_numpy(SCREAMING_SNAKE_CASE     )

    def get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:Optional[int]        =		f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __UpperCamelCase					:Dict        =		tf.train.load_variable(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        if "kernel" in name:
            __UpperCamelCase					:Tuple        =		array.transpose()

        return torch.from_numpy(SCREAMING_SNAKE_CASE     )

    def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:Any        =		f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
        __UpperCamelCase					:Dict        =		tf.train.load_variable(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Union[str, Any]        =		array.reshape(SCREAMING_SNAKE_CASE     )

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

        return torch.from_numpy(SCREAMING_SNAKE_CASE     )

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

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

        # Self-attention
        __UpperCamelCase					:BertSelfAttention        =		layer.attention.self

        __UpperCamelCase					:List[Any]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_query_dense/kernel'''  ,      self_attn.query.weight.data.shape     )
        __UpperCamelCase					:List[Any]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_query_dense/bias'''  ,      self_attn.query.bias.data.shape     )
        __UpperCamelCase					:List[Any]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_key_dense/kernel'''  ,      self_attn.key.weight.data.shape     )
        __UpperCamelCase					:List[str]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_key_dense/bias'''  ,      self_attn.key.bias.data.shape     )
        __UpperCamelCase					:Dict        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_value_dense/kernel'''  ,      self_attn.value.weight.data.shape     )
        __UpperCamelCase					:Optional[Any]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_value_dense/bias'''  ,      self_attn.value.bias.data.shape     )

        # Self-attention Output
        __UpperCamelCase					:BertSelfOutput        =		layer.attention.output

        __UpperCamelCase					:Dict        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_output_dense/kernel'''  ,      self_output.dense.weight.data.shape     )
        __UpperCamelCase					:List[str]        =		get_encoder_attention_layer_array(
            SCREAMING_SNAKE_CASE  ,      '''_output_dense/bias'''  ,      self_output.dense.bias.data.shape     )

        __UpperCamelCase					:Tuple        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_attention_layer_norm/gamma'''     )
        __UpperCamelCase					:Union[str, Any]        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_attention_layer_norm/beta'''     )

        # Intermediate
        __UpperCamelCase					:BertIntermediate        =		layer.intermediate

        __UpperCamelCase					:Optional[int]        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_intermediate_dense/kernel'''     )
        __UpperCamelCase					:Optional[int]        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_intermediate_dense/bias'''     )

        # Output
        __UpperCamelCase					:BertOutput        =		layer.output

        __UpperCamelCase					:Union[str, Any]        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_output_dense/kernel'''     )
        __UpperCamelCase					:Dict        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_output_dense/bias'''     )

        __UpperCamelCase					:Any        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_output_layer_norm/gamma'''     )
        __UpperCamelCase					:int        =		get_encoder_layer_array(SCREAMING_SNAKE_CASE  ,      '''_output_layer_norm/beta'''     )

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

    # LM Head
    __UpperCamelCase					:str        =		model.cls.predictions.transform

    __UpperCamelCase					:str        =		get_masked_lm_array('''dense/kernel'''     )
    __UpperCamelCase					:Optional[int]        =		get_masked_lm_array('''dense/bias'''     )

    __UpperCamelCase					:str        =		get_masked_lm_array('''layer_norm/gamma'''     )
    __UpperCamelCase					:Dict        =		get_masked_lm_array('''layer_norm/beta'''     )

    __UpperCamelCase					:List[Any]        =		get_masked_lm_array('''embedding_table'''     )

    # Pooling
    __UpperCamelCase					:List[str]        =		BertPooler(config=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:BertPooler        =		get_encoder_array('''_pooler_layer/kernel'''     )
    __UpperCamelCase					:BertPooler        =		get_encoder_array('''_pooler_layer/bias'''     )

    # Export final model
    model.save_pretrained(SCREAMING_SNAKE_CASE     )

    # Integration test - should load without any errors ;)
    __UpperCamelCase					:Dict        =		BertForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE     )
    print(new_model.eval()     )

    print('''Model conversion was done sucessfully!'''     )


if __name__ == "__main__":
    __lowercase										= 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.''',
    )
    __lowercase										= parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase										= '''▁'''

__lowercase										= get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						int      =				BertGenerationTokenizer
    a__     :						Dict      =				False
    a__     :						str      =				True







    def       UpperCamelCase__       (     self)	->     List[str]:
        super().setUp()

        __UpperCamelCase					:Optional[Any]        =		BertGenerationTokenizer(__lowercase			,			keep_accents=__lowercase)
        tokenizer.save_pretrained(self.tmpdirname)







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:Dict        =		'''<s>'''
        __UpperCamelCase					:Dict        =		1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase)			,			__lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase)			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Optional[Any]        =		list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]			,			'''<unk>''')
        self.assertEqual(vocab_keys[1]			,			'''<s>''')
        self.assertEqual(vocab_keys[-1]			,			'''<pad>''')
        self.assertEqual(len(__lowercase)			,			1_002)







    def       UpperCamelCase__       (     self)	->     Dict:
        self.assertEqual(self.get_tokenizer().vocab_size			,			1_000)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:str        =		BertGenerationTokenizer(__lowercase			,			keep_accents=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		tokenizer.tokenize('''This is a test''')
        self.assertListEqual(__lowercase			,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(__lowercase)			,			[285, 46, 10, 170, 382]			,			)

        __UpperCamelCase					:List[Any]        =		tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
        self.assertListEqual(
            __lowercase			,			[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''9''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''é''',
                '''.''',
            ]			,			)
        __UpperCamelCase					:Any        =		tokenizer.convert_tokens_to_ids(__lowercase)
        self.assertListEqual(
            __lowercase			,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,			)

        __UpperCamelCase					:Tuple        =		tokenizer.convert_ids_to_tokens(__lowercase)
        self.assertListEqual(
            __lowercase			,			[
                SPIECE_UNDERLINE + '''I''',
                SPIECE_UNDERLINE + '''was''',
                SPIECE_UNDERLINE + '''b''',
                '''or''',
                '''n''',
                SPIECE_UNDERLINE + '''in''',
                SPIECE_UNDERLINE + '''''',
                '''<unk>''',
                '''2''',
                '''0''',
                '''0''',
                '''0''',
                ''',''',
                SPIECE_UNDERLINE + '''and''',
                SPIECE_UNDERLINE + '''this''',
                SPIECE_UNDERLINE + '''is''',
                SPIECE_UNDERLINE + '''f''',
                '''al''',
                '''s''',
                '''<unk>''',
                '''.''',
            ]			,			)







    @cached_property
    def       UpperCamelCase__       (     self)	->     int:
        return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		'''Hello World!'''
        __UpperCamelCase					:Optional[int]        =		[18_536, 2_260, 101]

        self.assertListEqual(__lowercase			,			self.big_tokenizer.encode(__lowercase))







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Optional[Any]        =		(
            '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
            ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
        )
        __UpperCamelCase					:Union[str, Any]        =		[
            871,
            419,
            358,
            946,
            991,
            2_521,
            452,
            358,
            1_357,
            387,
            7_751,
            3_536,
            112,
            985,
            456,
            126,
            865,
            938,
            5_400,
            5_734,
            458,
            1_368,
            467,
            786,
            2_462,
            5_246,
            1_159,
            633,
            865,
            4_519,
            457,
            582,
            852,
            2_557,
            427,
            916,
            508,
            405,
            34_324,
            497,
            391,
            408,
            11_342,
            1_244,
            385,
            100,
            938,
            985,
            456,
            574,
            362,
            12_597,
            3_200,
            3_129,
            1_172,
        ]

        self.assertListEqual(__lowercase			,			self.big_tokenizer.encode(__lowercase))







    @require_torch
    @slow
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        import torch

        from transformers import BertGenerationConfig, BertGenerationEncoder

        # Build sequence
        __UpperCamelCase					:Optional[Any]        =		list(self.big_tokenizer.get_vocab().keys())[:10]
        __UpperCamelCase					:Optional[int]        =		''' '''.join(__lowercase)
        __UpperCamelCase					:Optional[int]        =		self.big_tokenizer.encode_plus(__lowercase			,			return_tensors='''pt'''			,			return_token_type_ids=__lowercase)
        __UpperCamelCase					:List[str]        =		self.big_tokenizer.batch_encode_plus(
            [sequence + ''' ''' + sequence]			,			return_tensors='''pt'''			,			return_token_type_ids=__lowercase)

        __UpperCamelCase					:List[Any]        =		BertGenerationConfig()
        __UpperCamelCase					:Optional[Any]        =		BertGenerationEncoder(__lowercase)

        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
            model(**__lowercase)
            model(**__lowercase)







    @slow
    def       UpperCamelCase__       (     self)	->     Dict:
        # fmt: off
        __UpperCamelCase					:List[str]        =		{'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__lowercase			,			model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''			,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''			,			)





