'''simple docstring'''





import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
					raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
					raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__UpperCamelCase	: Dict    		=  logging.get_logger(__name__)

__UpperCamelCase	: List[Any]    		=  'The Nymphenburg Palace is a beautiful palace in Munich!'
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					int           =							{
						    '''attention_cell''': '''multi_head''',
						    '''num_layers''': 4,
						    '''units''': 1_024,
						    '''hidden_size''': 768,
						    '''max_length''': 512,
						    '''num_heads''': 8,
						    '''scaled''': True,
						    '''dropout''': 0.1,
						    '''use_residual''': True,
						    '''embed_size''': 1_024,
						    '''embed_dropout''': 0.1,
						    '''word_embed''': None,
						    '''layer_norm_eps''': 1e-5,
						    '''token_type_vocab_size''': 2,
						}

						SCREAMING_SNAKE_CASE							:					List[str]           =							bort_4_8_768_1024_hparams

						# Let's construct the original Bort model here
						# Taken from official BERT implementation, see:
						# https://github.com/alexa/bort/blob/master/bort/bort.py
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							BERTEncoder(
						    attention_cell=predefined_args['''attention_cell''']	,						num_layers=predefined_args['''num_layers''']	,						units=predefined_args['''units''']	,						hidden_size=predefined_args['''hidden_size''']	,						max_length=predefined_args['''max_length''']	,						num_heads=predefined_args['''num_heads''']	,						scaled=predefined_args['''scaled''']	,						dropout=predefined_args['''dropout''']	,						output_attention=_lowercase	,						output_all_encodings=_lowercase	,						use_residual=predefined_args['''use_residual''']	,						activation=predefined_args.get('''activation'''	,						'''gelu'''		)	,						layer_norm_eps=predefined_args.get('''layer_norm_eps'''	,						_lowercase		)	,						)

						# Vocab information needs to be fetched first
						# It's the same as RoBERTa, so RobertaTokenizer can be used later
						SCREAMING_SNAKE_CASE							:					int           =							'''openwebtext_ccnews_stories_books_cased'''

						# Specify download folder to Gluonnlp's vocab
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							os.path.join(get_home_dir()	,						'''models'''		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							_load_vocab(_lowercase	,						_lowercase	,						_lowercase	,						cls=_lowercase		)

						SCREAMING_SNAKE_CASE							:					int           =							nlp.model.BERTModel(
						    _lowercase	,						len(_lowercase		)	,						units=predefined_args['''units''']	,						embed_size=predefined_args['''embed_size''']	,						embed_dropout=predefined_args['''embed_dropout''']	,						word_embed=predefined_args['''word_embed''']	,						use_pooler=_lowercase	,						use_token_type_embed=_lowercase	,						token_type_vocab_size=predefined_args['''token_type_vocab_size''']	,						use_classifier=_lowercase	,						use_decoder=_lowercase	,						)

						original_bort.load_parameters(_lowercase	,						cast_dtype=_lowercase	,						ignore_extra=_lowercase		)
						SCREAMING_SNAKE_CASE							:					int           =							original_bort._collect_params_with_prefix()

						# Build our config 🤗
						SCREAMING_SNAKE_CASE							:					Any           =							{
						    '''architectures''': ['''BertForMaskedLM'''],
						    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
						    '''hidden_act''': '''gelu''',
						    '''hidden_dropout_prob''': predefined_args['''dropout'''],
						    '''hidden_size''': predefined_args['''embed_size'''],
						    '''initializer_range''': 0.02,
						    '''intermediate_size''': predefined_args['''hidden_size'''],
						    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
						    '''max_position_embeddings''': predefined_args['''max_length'''],
						    '''model_type''': '''bort''',
						    '''num_attention_heads''': predefined_args['''num_heads'''],
						    '''num_hidden_layers''': predefined_args['''num_layers'''],
						    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
						    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
						    '''vocab_size''': len(_lowercase		),
						}

						SCREAMING_SNAKE_CASE							:					List[str]           =							BertConfig.from_dict(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							BertForMaskedLM(_lowercase		)
						hf_bort_model.eval()

						# Parameter mapping table (Gluonnlp to Transformers)
						# * denotes layer index
						#
						# | Gluon Parameter                                                | Transformers Parameter
						# | -------------------------------------------------------------- | ----------------------
						# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
						# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
						# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
						# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
						# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
						# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
						# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
						# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
						# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
						# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
						# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

						# Helper function to convert MXNET Arrays to PyTorch
						def to_torch(_lowercase		) -> nn.Parameter:
												return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()		)		)

						# Check param shapes and map new HF param back
						def check_and_map_params(_lowercase	,						_lowercase		):
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							hf_param.shape

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							to_torch(params[gluon_param]		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							gluon_param.shape

												assert (
												    shape_hf == shape_gluon
												), f"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""

												return gluon_param

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight	,						'''word_embed.0.weight'''		)
						SCREAMING_SNAKE_CASE							:					int           =							check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight	,						'''encoder.position_weight'''		)
						SCREAMING_SNAKE_CASE							:					Dict           =							check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias	,						'''encoder.layer_norm.beta'''		)
						SCREAMING_SNAKE_CASE							:					int           =							check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.weight	,						'''encoder.layer_norm.gamma'''		)

						# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

						for i in range(hf_bort_config.num_hidden_layers		):
												SCREAMING_SNAKE_CASE							:					BertLayer           =							hf_bort_model.bert.encoder.layer[i]

												# self attention
												SCREAMING_SNAKE_CASE							:					BertSelfAttention           =							layer.attention.self

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
												    self_attn.key.bias.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""		)

												SCREAMING_SNAKE_CASE							:					int           =							check_and_map_params(
												    self_attn.key.weight.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							check_and_map_params(
												    self_attn.query.bias.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							check_and_map_params(
												    self_attn.query.weight.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							check_and_map_params(
												    self_attn.value.bias.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							check_and_map_params(
												    self_attn.value.weight.data	,						f"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""		)

												# self attention output
												SCREAMING_SNAKE_CASE							:					BertSelfOutput           =							layer.attention.output

												SCREAMING_SNAKE_CASE							:					List[str]           =							check_and_map_params(
												    self_output.dense.bias	,						f"""encoder.transformer_cells.{i}.proj.bias"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							check_and_map_params(
												    self_output.dense.weight	,						f"""encoder.transformer_cells.{i}.proj.weight"""		)
												SCREAMING_SNAKE_CASE							:					str           =							check_and_map_params(
												    self_output.LayerNorm.bias	,						f"""encoder.transformer_cells.{i}.layer_norm.beta"""		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
												    self_output.LayerNorm.weight	,						f"""encoder.transformer_cells.{i}.layer_norm.gamma"""		)

												# intermediate
												SCREAMING_SNAKE_CASE							:					BertIntermediate           =							layer.intermediate

												SCREAMING_SNAKE_CASE							:					Tuple           =							check_and_map_params(
												    intermediate.dense.bias	,						f"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
												    intermediate.dense.weight	,						f"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""		)

												# output
												SCREAMING_SNAKE_CASE							:					BertOutput           =							layer.output

												SCREAMING_SNAKE_CASE							:					int           =							check_and_map_params(
												    bert_output.dense.bias	,						f"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
												    bert_output.dense.weight	,						f"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""		)
												SCREAMING_SNAKE_CASE							:					Any           =							check_and_map_params(
												    bert_output.LayerNorm.bias	,						f"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							check_and_map_params(
												    bert_output.LayerNorm.weight	,						f"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma"""		)

						# Save space and energy 🎄
						hf_bort_model.half()

						# Compare output of both models
						SCREAMING_SNAKE_CASE							:					int           =							RobertaTokenizer.from_pretrained('''roberta-base'''		)

						SCREAMING_SNAKE_CASE							:					Any           =							tokenizer.encode_plus(_lowercase		)['''input_ids''']

						# Get gluon output
						SCREAMING_SNAKE_CASE							:					Tuple           =							mx.nd.array([input_ids]		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							original_bort(inputs=_lowercase	,						token_types=[]		)

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							BertModel.from_pretrained(_lowercase		)
						hf_bort_model.eval()

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer.encode_plus(_lowercase	,						return_tensors='''pt'''		)
						SCREAMING_SNAKE_CASE							:					str           =							hf_bort_model(**_lowercase		)[0]

						SCREAMING_SNAKE_CASE							:					Dict           =							output_gluon[0].asnumpy()
						SCREAMING_SNAKE_CASE							:					List[str]           =							output_hf[0].detach().numpy()

						SCREAMING_SNAKE_CASE							:					str           =							np.max(np.abs(hf_layer - gluon_layer		)		).item()
						SCREAMING_SNAKE_CASE							:					List[str]           =							np.allclose(_lowercase	,						_lowercase	,						atol=1e-3		)

						if success:
												print('''✔️ Both model do output the same tensors'''		)
						else:
												print('''❌ Both model do **NOT** output the same tensors'''		)
												print('''Absolute difference is:'''	,						_lowercase		)


if __name__ == "__main__":
					__UpperCamelCase	: Optional[Any]    		=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					__UpperCamelCase	: Optional[Any]    		=  parser.parse_args()
					convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							analyze_text(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							list(''' ''' + ascii_lowercase		)
						# what is our total sum of probabilities.
						SCREAMING_SNAKE_CASE							:					Tuple           =							sum(single_char_strings.values()		)

						# one length string
						SCREAMING_SNAKE_CASE							:					Tuple           =							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:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							single_char_strings[ch]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							my_str / all_sum
																		my_fir_sum += prob * math.loga(_lowercase		)  # entropy formula.

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

						# two len string
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sum(two_char_strings.values()		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							cha + cha
																		if sequence in two_char_strings:
																								SCREAMING_SNAKE_CASE							:					Any           =							two_char_strings[sequence]
																								SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		) / all_sum
																								my_sec_sum += prob * math.loga(_lowercase		)

    # 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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							Counter()  # type: ignore
						SCREAMING_SNAKE_CASE							:					Any           =							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(_lowercase		) - 1		):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings
def    A			(					):
						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()





