
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()
_A   =			logging.get_logger(__name__)

_A   =			'The Nymphenburg Palace is a beautiful palace in Munich!'



def 						lowercase_      (       A__			,		A__		)							->							Optional[Any]:
    """simple docstring"""

    snake_case								= {
        "attention_cell": "multi_head",
        "num_layers": 4,
        "units": 1024,
        "hidden_size": 768,
        "max_length": 512,
        "num_heads": 8,
        "scaled": True,
        "dropout": 0.1,
        "use_residual": True,
        "embed_size": 1024,
        "embed_dropout": 0.1,
        "word_embed": None,
        "layer_norm_eps": 1e-5,
        "token_type_vocab_size": 2,
    }

    snake_case								= 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
    snake_case								= 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=_A			,		output_all_encodings=_A			,		use_residual=predefined_args["use_residual"]			,		activation=predefined_args.get("activation"			,		"gelu"		)			,		layer_norm_eps=predefined_args.get("layer_norm_eps"			,		_A		)			,		)

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

    # Specify download folder to Gluonnlp's vocab
    snake_case								= os.path.join(get_home_dir()			,		"models"		)
    snake_case								= _load_vocab(_A			,		_A			,		_A			,		cls=_A		)

    snake_case								= nlp.model.BERTModel(
        _A			,		len(_A		)			,		units=predefined_args["units"]			,		embed_size=predefined_args["embed_size"]			,		embed_dropout=predefined_args["embed_dropout"]			,		word_embed=predefined_args["word_embed"]			,		use_pooler=_A			,		use_token_type_embed=_A			,		token_type_vocab_size=predefined_args["token_type_vocab_size"]			,		use_classifier=_A			,		use_decoder=_A			,		)

    original_bort.load_parameters(_A			,		cast_dtype=_A			,		ignore_extra=_A		)
    snake_case								= original_bort._collect_params_with_prefix()

    # Build our config 🤗
    snake_case								= {
        "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(_A		),
    }

    snake_case								= BertConfig.from_dict(_A		)
    snake_case								= BertForMaskedLM(_A		)
    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(A__		) -> 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(A__			,		A__		):
        snake_case								= hf_param.shape

        snake_case								= to_torch(params[gluon_param]		)
        snake_case								= 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

    snake_case								= check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight			,		"word_embed.0.weight"		)
    snake_case								= check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight			,		"encoder.position_weight"		)
    snake_case								= check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias			,		"encoder.layer_norm.beta"		)
    snake_case								= 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)
    snake_case								= torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

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

        # self attention
        snake_case								= layer.attention.self

        snake_case								= check_and_map_params(
            self_attn.key.bias.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'		)

        snake_case								= check_and_map_params(
            self_attn.key.weight.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'		)
        snake_case								= check_and_map_params(
            self_attn.query.bias.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'		)
        snake_case								= check_and_map_params(
            self_attn.query.weight.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'		)
        snake_case								= check_and_map_params(
            self_attn.value.bias.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'		)
        snake_case								= check_and_map_params(
            self_attn.value.weight.data			,		F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'		)

        # self attention output
        snake_case								= layer.attention.output

        snake_case								= check_and_map_params(
            self_output.dense.bias			,		F'encoder.transformer_cells.{i}.proj.bias'		)
        snake_case								= check_and_map_params(
            self_output.dense.weight			,		F'encoder.transformer_cells.{i}.proj.weight'		)
        snake_case								= check_and_map_params(
            self_output.LayerNorm.bias			,		F'encoder.transformer_cells.{i}.layer_norm.beta'		)
        snake_case								= check_and_map_params(
            self_output.LayerNorm.weight			,		F'encoder.transformer_cells.{i}.layer_norm.gamma'		)

        # intermediate
        snake_case								= layer.intermediate

        snake_case								= check_and_map_params(
            intermediate.dense.bias			,		F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'		)
        snake_case								= check_and_map_params(
            intermediate.dense.weight			,		F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'		)

        # output
        snake_case								= layer.output

        snake_case								= check_and_map_params(
            bert_output.dense.bias			,		F'encoder.transformer_cells.{i}.ffn.ffn_2.bias'		)
        snake_case								= check_and_map_params(
            bert_output.dense.weight			,		F'encoder.transformer_cells.{i}.ffn.ffn_2.weight'		)
        snake_case								= check_and_map_params(
            bert_output.LayerNorm.bias			,		F'encoder.transformer_cells.{i}.ffn.layer_norm.beta'		)
        snake_case								= 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
    snake_case								= RobertaTokenizer.from_pretrained("roberta-base"		)

    snake_case								= tokenizer.encode_plus(_A		)["input_ids"]

    # Get gluon output
    snake_case								= mx.nd.array([input_ids]		)
    snake_case								= original_bort(inputs=_A			,		token_types=[]		)

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(_A		)
    snake_case								= BertModel.from_pretrained(_A		)
    hf_bort_model.eval()

    snake_case								= tokenizer.encode_plus(_A			,		return_tensors="pt"		)
    snake_case								= hf_bort_model(**_A		)[0]

    snake_case								= output_gluon[0].asnumpy()
    snake_case								= output_hf[0].detach().numpy()

    snake_case								= np.max(np.abs(hf_layer - gluon_layer		)		).item()
    snake_case								= np.allclose(_A			,		_A			,		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:"			,		_A		)


if __name__ == "__main__":
   _A   =			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."
   )
   _A   =			parser.parse_args()
   convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




from collections import defaultdict







class 					lowerCamelCase		:





     def __init__(self				:   Tuple    ,   _A				:   Optional[int]    ,   _A				:   List[str]       )						->						Union[str, Any]:
         snake_case								= total  # total no of tasks (N)

         # DP table will have a dimension of (2^M)*N
         # initially all values are set to -1
         snake_case								= [
             [-1 for i in range(total + 1       )] for j in range(2 ** len(_A       )       )
         ]

         snake_case								= defaultdict(_A       )  # stores the list of persons for each task

         # final_mask is used to check if all persons are included by setting all bits
         # to 1
         snake_case								= (1 << len(_A       )) - 1





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Optional[Any]    ,   _A				:   List[Any]       )						->						str:
         # if mask == self.finalmask all persons are distributed tasks, return 1
         if mask == self.final_mask:
             return 1

         # if not everyone gets the task and no more tasks are available, return 0
         if task_no > self.total_tasks:
             return 0

         # if case already considered
         if self.dp[mask][task_no] != -1:
             return self.dp[mask][task_no]

         # Number of ways when we don't this task in the arrangement
         snake_case								= self.count_ways_until(_A    ,   task_no + 1       )

         # now assign the tasks one by one to all possible persons and recursively
         # assign for the remaining tasks.
         if task_no in self.task:
             for p in self.task[task_no]:
                 # if p is already given a task
                 if mask & (1 << p):
                     continue

                 # assign this task to p and change the mask value. And recursively
                 # assign tasks with the new mask value.
                 total_ways_util += self.count_ways_until(mask | (1 << p)    ,   task_no + 1       )

        # save the value.
         snake_case								= total_ways_util

         return self.dp[mask][task_no]





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Dict       )						->						Optional[Any]:
         # Store the list of persons for each task
         for i in range(len(_A       )       ):
             for j in task_performed[i]:
                 self.task[j].append(_A       )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
         return self.count_ways_until(0    ,   1       )


if __name__ == "__main__":
   _A   =			5  # total no of tasks (the value of N)

   # the list of tasks that can be done by M persons.
   _A   =			[[1, 3, 4], [1, 2, 5], [3, 4]]
   print(
       AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
           task_performed
       )
   )

