




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       (     _snake_case						, _snake_case						, _snake_case							):
							def get_masked_lm_array(_snake_case							):
														lowerCAmelCase   =		F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														lowerCAmelCase   =		tf.train.load_variable(_snake_case						, _snake_case							)

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

														return torch.from_numpy(_snake_case							)

							def get_encoder_array(_snake_case							):
														lowerCAmelCase   =		F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														lowerCAmelCase   =		tf.train.load_variable(_snake_case						, _snake_case							)

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

														return torch.from_numpy(_snake_case							)

							def get_encoder_layer_array(_snake_case						, _snake_case							):
														lowerCAmelCase   =		F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														lowerCAmelCase   =		tf.train.load_variable(_snake_case						, _snake_case							)

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

														return torch.from_numpy(_snake_case							)

							def get_encoder_attention_layer_array(_snake_case						, _snake_case						, _snake_case							):
														lowerCAmelCase   =		F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														lowerCAmelCase   =		tf.train.load_variable(_snake_case						, _snake_case							)
														lowerCAmelCase   =		array.reshape(_snake_case							)

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

														return torch.from_numpy(_snake_case							)

							print(F"""Loading model based on config from {config_path}..."""							)
							lowerCAmelCase   =		BertConfig.from_json_file(_snake_case							)
							lowerCAmelCase   =		BertForMaskedLM(_snake_case							)

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

														# Self-attention
														lowerCAmelCase   =		layer.attention.self

														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_query_dense/kernel'''						, self_attn.query.weight.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_query_dense/bias'''						, self_attn.query.bias.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_key_dense/kernel'''						, self_attn.key.weight.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_key_dense/bias'''						, self_attn.key.bias.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_value_dense/kernel'''						, self_attn.value.weight.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_value_dense/bias'''						, self_attn.value.bias.data.shape							)

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

														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_output_dense/kernel'''						, self_output.dense.weight.data.shape							)
														lowerCAmelCase   =		get_encoder_attention_layer_array(
														    _snake_case						, '''_output_dense/bias'''						, self_output.dense.bias.data.shape							)

														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_attention_layer_norm/gamma'''							)
														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_attention_layer_norm/beta'''							)

														# Intermediate
														lowerCAmelCase   =		layer.intermediate

														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_intermediate_dense/kernel'''							)
														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_intermediate_dense/bias'''							)

														# Output
														lowerCAmelCase   =		layer.output

														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_output_dense/kernel'''							)
														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_output_dense/bias'''							)

														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_output_layer_norm/gamma'''							)
														lowerCAmelCase   =		get_encoder_layer_array(_snake_case						, '''_output_layer_norm/beta'''							)

							# Embeddings
							lowerCAmelCase   =		get_encoder_array('''_position_embedding_layer/embeddings'''							)
							lowerCAmelCase   =		get_encoder_array('''_type_embedding_layer/embeddings'''							)
							lowerCAmelCase   =		get_encoder_array('''_embedding_norm_layer/gamma'''							)
							lowerCAmelCase   =		get_encoder_array('''_embedding_norm_layer/beta'''							)

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

							lowerCAmelCase   =		get_masked_lm_array('''dense/kernel'''							)
							lowerCAmelCase   =		get_masked_lm_array('''dense/bias'''							)

							lowerCAmelCase   =		get_masked_lm_array('''layer_norm/gamma'''							)
							lowerCAmelCase   =		get_masked_lm_array('''layer_norm/beta'''							)

							lowerCAmelCase   =		get_masked_lm_array('''embedding_table'''							)

							# Pooling
							lowerCAmelCase   =		BertPooler(config=_snake_case							)
							lowerCAmelCase   =		get_encoder_array('''_pooler_layer/kernel'''							)
							lowerCAmelCase   =		get_encoder_array('''_pooler_layer/bias'''							)

							# Export final model
							model.save_pretrained(_snake_case							)

							# Integration test - should load without any errors ;)
							lowerCAmelCase   =		BertForMaskedLM.from_pretrained(_snake_case							)
							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)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						={
    """configuration_jukebox""": [
        """JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """JukeboxConfig""",
        """JukeboxPriorConfig""",
        """JukeboxVQVAEConfig""",
    ],
    """tokenization_jukebox""": ["""JukeboxTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """JukeboxModel""",
		    """JukeboxPreTrainedModel""",
		    """JukeboxVQVAE""",
		    """JukeboxPrior""",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



