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

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

          return torch.from_numpy(lowercase_				)

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

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

          return torch.from_numpy(lowercase_				)

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

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

          return torch.from_numpy(lowercase_				)

     def get_encoder_attention_layer_array(A			,				A			,				A				):
          UpperCamelCase__					    =   f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__					    =   tf.train.load_variable(lowercase_			,				lowercase_				)
          UpperCamelCase__					    =   array.reshape(lowercase_				)

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

          return torch.from_numpy(lowercase_				)

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

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

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

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

          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_attention_layer_norm/gamma'''				)
          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_attention_layer_norm/beta'''				)

          # Intermediate
          UpperCamelCase__					    =   layer.intermediate

          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_intermediate_dense/kernel'''				)
          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_intermediate_dense/bias'''				)

          # Output
          UpperCamelCase__					    =   layer.output

          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_output_dense/kernel'''				)
          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_output_dense/bias'''				)

          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_output_layer_norm/gamma'''				)
          UpperCamelCase__					    =   get_encoder_layer_array(lowercase_			,				'''_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=lowercase_				)
     UpperCamelCase__					    =   get_encoder_array('''_pooler_layer/kernel'''				)
     UpperCamelCase__					    =   get_encoder_array('''_pooler_layer/bias'''				)

     # Export final model
     model.save_pretrained(lowercase_				)

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

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


if __name__ == "__main__":
     __magic_name__    		=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.''',
     )
     __magic_name__    		=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_tf_available, is_torch_available


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



