"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							def get_masked_lm_array(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														_SCREAMING_SNAKE_CASE		:						str			  =    tf.train.load_variable(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

														return torch.from_numpy(__SCREAMING_SNAKE_CASE  )

							def get_encoder_array(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														_SCREAMING_SNAKE_CASE		:						Any			  =    tf.train.load_variable(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

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

														return torch.from_numpy(__SCREAMING_SNAKE_CASE  )

							def get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														_SCREAMING_SNAKE_CASE		:						str			  =    tf.train.load_variable(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

														if "kernel" in name:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    array.transpose()

														return torch.from_numpy(__SCREAMING_SNAKE_CASE  )

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

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

														return torch.from_numpy(__SCREAMING_SNAKE_CASE  )

							print(F"""Loading model based on config from {config_path}..."""  )
							_SCREAMING_SNAKE_CASE		:						int			  =    BertConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    BertForMaskedLM(__SCREAMING_SNAKE_CASE  )

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

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

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_query_dense/kernel"""     ,	self_attn.query.weight.data.shape  )
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_query_dense/bias"""     ,	self_attn.query.bias.data.shape  )
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_key_dense/kernel"""     ,	self_attn.key.weight.data.shape  )
														_SCREAMING_SNAKE_CASE		:						Dict			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_key_dense/bias"""     ,	self_attn.key.bias.data.shape  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_value_dense/kernel"""     ,	self_attn.value.weight.data.shape  )
														_SCREAMING_SNAKE_CASE		:						Any			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_value_dense/bias"""     ,	self_attn.value.bias.data.shape  )

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

														_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_output_dense/kernel"""     ,	self_output.dense.weight.data.shape  )
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_encoder_attention_layer_array(
														    __SCREAMING_SNAKE_CASE     ,	"""_output_dense/bias"""     ,	self_output.dense.bias.data.shape  )

														_SCREAMING_SNAKE_CASE		:						Dict			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_attention_layer_norm/gamma"""  )
														_SCREAMING_SNAKE_CASE		:						str			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_attention_layer_norm/beta"""  )

														# Intermediate
														_SCREAMING_SNAKE_CASE		:						BertIntermediate			  =    layer.intermediate

														_SCREAMING_SNAKE_CASE		:						str			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_intermediate_dense/kernel"""  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_intermediate_dense/bias"""  )

														# Output
														_SCREAMING_SNAKE_CASE		:						BertOutput			  =    layer.output

														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_output_dense/kernel"""  )
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_output_dense/bias"""  )

														_SCREAMING_SNAKE_CASE		:						Dict			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_output_layer_norm/gamma"""  )
														_SCREAMING_SNAKE_CASE		:						str			  =    get_encoder_layer_array(__SCREAMING_SNAKE_CASE     ,	"""_output_layer_norm/beta"""  )

							# Embeddings
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    get_encoder_array("""_position_embedding_layer/embeddings"""  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_encoder_array("""_type_embedding_layer/embeddings"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_encoder_array("""_embedding_norm_layer/gamma"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_encoder_array("""_embedding_norm_layer/beta"""  )

							# LM Head
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model.cls.predictions.transform

							_SCREAMING_SNAKE_CASE		:						str			  =    get_masked_lm_array("""dense/kernel"""  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    get_masked_lm_array("""dense/bias"""  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    get_masked_lm_array("""layer_norm/gamma"""  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_masked_lm_array("""layer_norm/beta"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_masked_lm_array("""embedding_table"""  )

							# Pooling
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    BertPooler(config=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						BertPooler			  =    get_encoder_array("""_pooler_layer/kernel"""  )
							_SCREAMING_SNAKE_CASE		:						BertPooler			  =    get_encoder_array("""_pooler_layer/bias"""  )

							# Export final model
							model.save_pretrained(__SCREAMING_SNAKE_CASE  )

							# Integration test - should load without any errors ;)
							_SCREAMING_SNAKE_CASE		:						int			  =    BertForMaskedLM.from_pretrained(__SCREAMING_SNAKE_CASE  )
							print(new_model.eval()  )

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


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



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     =	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''UniSpeechForCTC''',
			    '''UniSpeechForPreTraining''',
			    '''UniSpeechForSequenceClassification''',
			    '''UniSpeechModel''',
			    '''UniSpeechPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_unispeech import (
						    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
						    UniSpeechForCTC,
						    UniSpeechForPreTraining,
						    UniSpeechForSequenceClassification,
						    UniSpeechModel,
						    UniSpeechPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


