




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_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: str					,	UpperCamelCase__							: str )	->			List[Any]:





				"""simple docstring"""


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

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

								return torch.from_numpy(UpperCamelCase__ )

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

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

								return torch.from_numpy(UpperCamelCase__ )

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

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

								return torch.from_numpy(UpperCamelCase__ )

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

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

								return torch.from_numpy(UpperCamelCase__ )

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

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

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

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

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

								# Intermediate
								__lowerCamelCase									=       layer.intermediate

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

								# Output
								__lowerCamelCase									=       layer.output

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

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

				# Export final model
				model.save_pretrained(UpperCamelCase__ )

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

				print('Model conversion was done sucessfully!' )


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








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


