





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       ):






							"""simple docstring"""





							def get_masked_lm_array(a__   :str       ):
														UpperCamelCase__				     =			f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )

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

														return torch.from_numpy(a__       )

							def get_encoder_array(a__   :str       ):
														UpperCamelCase__				     =			f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )

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

														return torch.from_numpy(a__       )

							def get_encoder_layer_array(a__   :int   ,			a__   :str       ):
														UpperCamelCase__				     =			f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )

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

														return torch.from_numpy(a__       )

							def get_encoder_attention_layer_array(a__   :int   ,			a__   :str   ,			a__   :List[str]       ):
														UpperCamelCase__				     =			f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )
														UpperCamelCase__				     =			array.reshape(a__       )

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

														return torch.from_numpy(a__       )

							print(f"""Loading model based on config from {config_path}..."""       )
							UpperCamelCase__				     =			BertConfig.from_json_file(a__       )
							UpperCamelCase__				     =			BertForMaskedLM(a__       )

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

														# Self-attention
														UpperCamelCase__				     =			layer.attention.self

														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_query_dense/kernel"""   ,			self_attn.query.weight.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_query_dense/bias"""   ,			self_attn.query.bias.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_key_dense/kernel"""   ,			self_attn.key.weight.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_key_dense/bias"""   ,			self_attn.key.bias.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_value_dense/kernel"""   ,			self_attn.value.weight.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_value_dense/bias"""   ,			self_attn.value.bias.data.shape       )

														# Self-attention Output
														UpperCamelCase__				     =			layer.attention.output

														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_output_dense/kernel"""   ,			self_output.dense.weight.data.shape       )
														UpperCamelCase__				     =			get_encoder_attention_layer_array(
														    a__   ,			"""_output_dense/bias"""   ,			self_output.dense.bias.data.shape       )

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_attention_layer_norm/gamma"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_attention_layer_norm/beta"""       )

														# Intermediate
														UpperCamelCase__				     =			layer.intermediate

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_intermediate_dense/kernel"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_intermediate_dense/bias"""       )

														# Output
														UpperCamelCase__				     =			layer.output

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_dense/kernel"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_dense/bias"""       )

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_layer_norm/gamma"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_layer_norm/beta"""       )

							# Embeddings
							UpperCamelCase__				     =			get_encoder_array("""_position_embedding_layer/embeddings"""       )
							UpperCamelCase__				     =			get_encoder_array("""_type_embedding_layer/embeddings"""       )
							UpperCamelCase__				     =			get_encoder_array("""_embedding_norm_layer/gamma"""       )
							UpperCamelCase__				     =			get_encoder_array("""_embedding_norm_layer/beta"""       )

							# LM Head
							UpperCamelCase__				     =			model.cls.predictions.transform

							UpperCamelCase__				     =			get_masked_lm_array("""dense/kernel"""       )
							UpperCamelCase__				     =			get_masked_lm_array("""dense/bias"""       )

							UpperCamelCase__				     =			get_masked_lm_array("""layer_norm/gamma"""       )
							UpperCamelCase__				     =			get_masked_lm_array("""layer_norm/beta"""       )

							UpperCamelCase__				     =			get_masked_lm_array("""embedding_table"""       )

							# Pooling
							UpperCamelCase__				     =			BertPooler(config=a__       )
							UpperCamelCase__				     =			get_encoder_array("""_pooler_layer/kernel"""       )
							UpperCamelCase__				     =			get_encoder_array("""_pooler_layer/bias"""       )

							# Export final model
							model.save_pretrained(a__       )

							# Integration test - should load without any errors ;)
							UpperCamelCase__				     =			BertForMaskedLM.from_pretrained(a__       )
							print(new_model.eval()       )

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


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











import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    """files"""   ,			[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]   ,			)
def     _UpperCamelCase     (a__   :Any   ,			a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			tmp_path_factory.mktemp("""dset_infos_dir"""       )
							if "full:README.md" in files:
														with open(dataset_infos_dir / """README.md"""   ,			"""w"""       ) as f:
																					f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""       )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / """README.md"""   ,			"""w"""       ) as f:
																					f.write(""""""       )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / """dataset_infos.json"""   ,			"""w"""       ) as f:
																					f.write("""{\"default\": {\"dataset_size\": 42}}"""       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    """dataset_info"""   ,			[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train"""}]   ,			download_size=42   ,			),
    ]   ,			)
def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :DatasetInfo       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_info.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfo.from_directory(a__       )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(a__   ,			"""dataset_info.json"""       )       )





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			DatasetInfo(
							    description="""foo"""   ,			citation="""bar"""   ,			homepage="""https://foo.bar"""   ,			license="""CC0"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			post_processed={}   ,			supervised_keys=()   ,			task_templates=[]   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train""", """num_examples""": 42}]   ,			download_checksums={}   ,			download_size=1337   ,			post_processing_size=442   ,			dataset_size=1234   ,			size_in_bytes=1337 + 442 + 1234   ,			)
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert sorted(a__       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]   ,			(list, dict, int, str)       )
							UpperCamelCase__				     =			yaml.safe_dump(a__       )
							UpperCamelCase__				     =			yaml.safe_load(a__       )
							assert dataset_info_yaml_dict == reloaded





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			DatasetInfo()
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    """dataset_infos_dict"""   ,			[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}       ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""   ,			features=Features({"""a""": Value("""int32"""       )}       )   ,			builder_name="""builder"""   ,			config_name="""config"""   ,			version="""1.0.0"""   ,			splits=[{"""name""": """train"""}]   ,			download_size=42   ,			)
            }       ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42       ),
                """v2""": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]   ,			)
def     _UpperCamelCase     (a__   :int   ,			a__   :DatasetInfosDict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_infos_dict.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														UpperCamelCase__				     =			config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														UpperCamelCase__				     =			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(a__   ,			"""README.md"""       )       )




