


'''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()
__A						=logging.get_logger(__name__)

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







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__       ):
			UpperCAmelCase__	:       Union[str, Any]   =  {
			    """attention_cell""": """multi_head""",
			    """num_layers""": 4,
			    """units""": 1_0_2_4,
			    """hidden_size""": 7_6_8,
			    """max_length""": 5_1_2,
			    """num_heads""": 8,
			    """scaled""": True,
			    """dropout""": 0.1,
			    """use_residual""": True,
			    """embed_size""": 1_0_2_4,
			    """embed_dropout""": 0.1,
			    """word_embed""": None,
			    """layer_norm_eps""": 1e-5,
			    """token_type_vocab_size""": 2,
			}

			UpperCAmelCase__	:       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
			UpperCAmelCase__	:       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=UpperCamelCase__			,   output_all_encodings=UpperCamelCase__			,   use_residual=predefined_args["""use_residual"""]			,   activation=predefined_args.get("""activation"""			,   """gelu"""       )			,   layer_norm_eps=predefined_args.get("""layer_norm_eps"""			,   UpperCamelCase__       )			,   )

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

			# Specify download folder to Gluonnlp's vocab
			UpperCAmelCase__	:       Any   =  os.path.join(get_home_dir()			,   """models"""       )
			UpperCAmelCase__	:       Tuple   =  _load_vocab(UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   cls=UpperCamelCase__       )

			UpperCAmelCase__	:       List[str]   =  nlp.model.BERTModel(
			    UpperCamelCase__			,   len(UpperCamelCase__       )			,   units=predefined_args["""units"""]			,   embed_size=predefined_args["""embed_size"""]			,   embed_dropout=predefined_args["""embed_dropout"""]			,   word_embed=predefined_args["""word_embed"""]			,   use_pooler=UpperCamelCase__			,   use_token_type_embed=UpperCamelCase__			,   token_type_vocab_size=predefined_args["""token_type_vocab_size"""]			,   use_classifier=UpperCamelCase__			,   use_decoder=UpperCamelCase__			,   )

			original_bort.load_parameters(UpperCamelCase__			,   cast_dtype=UpperCamelCase__			,   ignore_extra=UpperCamelCase__       )
			UpperCAmelCase__	:       Optional[int]   =  original_bort._collect_params_with_prefix()

			# Build our config 🤗
			UpperCAmelCase__	:       Dict   =  {
			    """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(UpperCamelCase__       ),
			}

			UpperCAmelCase__	:       int   =  BertConfig.from_dict(UpperCamelCase__       )
			UpperCAmelCase__	:       Optional[int]   =  BertForMaskedLM(UpperCamelCase__       )
			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(UpperCamelCase__       ) -> 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(UpperCamelCase__			,   UpperCamelCase__       ):
						UpperCAmelCase__	:       Tuple   =  hf_param.shape

						UpperCAmelCase__	:       Tuple   =  to_torch(params[gluon_param]       )
						UpperCAmelCase__	:       int   =  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

			UpperCAmelCase__	:       Tuple   =  check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight			,   """word_embed.0.weight"""       )
			UpperCAmelCase__	:       List[str]   =  check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight			,   """encoder.position_weight"""       )
			UpperCAmelCase__	:       List[str]   =  check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias			,   """encoder.layer_norm.beta"""       )
			UpperCAmelCase__	:       str   =  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)
			UpperCAmelCase__	:       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       ):
						UpperCAmelCase__	:       int   =  hf_bort_model.bert.encoder.layer[i]

						# self attention
						UpperCAmelCase__	:       int   =  layer.attention.self

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

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

						# self attention output
						UpperCAmelCase__	:       List[Any]   =  layer.attention.output

						UpperCAmelCase__	:       Tuple   =  check_and_map_params(
						    self_output.dense.bias			,   f'''encoder.transformer_cells.{i}.proj.bias'''       )
						UpperCAmelCase__	:       str   =  check_and_map_params(
						    self_output.dense.weight			,   f'''encoder.transformer_cells.{i}.proj.weight'''       )
						UpperCAmelCase__	:       Union[str, Any]   =  check_and_map_params(
						    self_output.LayerNorm.bias			,   f'''encoder.transformer_cells.{i}.layer_norm.beta'''       )
						UpperCAmelCase__	:       List[Any]   =  check_and_map_params(
						    self_output.LayerNorm.weight			,   f'''encoder.transformer_cells.{i}.layer_norm.gamma'''       )

						# intermediate
						UpperCAmelCase__	:       Dict   =  layer.intermediate

						UpperCAmelCase__	:       Union[str, Any]   =  check_and_map_params(
						    intermediate.dense.bias			,   f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''       )
						UpperCAmelCase__	:       Any   =  check_and_map_params(
						    intermediate.dense.weight			,   f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''       )

						# output
						UpperCAmelCase__	:       Tuple   =  layer.output

						UpperCAmelCase__	:       Any   =  check_and_map_params(
						    bert_output.dense.bias			,   f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''       )
						UpperCAmelCase__	:       List[str]   =  check_and_map_params(
						    bert_output.dense.weight			,   f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''       )
						UpperCAmelCase__	:       Union[str, Any]   =  check_and_map_params(
						    bert_output.LayerNorm.bias			,   f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''       )
						UpperCAmelCase__	:       Dict   =  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
			UpperCAmelCase__	:       Dict   =  RobertaTokenizer.from_pretrained("""roberta-base"""       )

			UpperCAmelCase__	:       Optional[int]   =  tokenizer.encode_plus(UpperCamelCase__       )["""input_ids"""]

			# Get gluon output
			UpperCAmelCase__	:       str   =  mx.nd.array([input_ids]       )
			UpperCAmelCase__	:       Optional[int]   =  original_bort(inputs=UpperCamelCase__			,   token_types=[]       )

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(UpperCamelCase__       )
			UpperCAmelCase__	:       List[str]   =  BertModel.from_pretrained(UpperCamelCase__       )
			hf_bort_model.eval()

			UpperCAmelCase__	:       Any   =  tokenizer.encode_plus(UpperCamelCase__			,   return_tensors="""pt"""       )
			UpperCAmelCase__	:       List[Any]   =  hf_bort_model(**UpperCamelCase__       )[0]

			UpperCAmelCase__	:       str   =  output_gluon[0].asnumpy()
			UpperCAmelCase__	:       int   =  output_hf[0].detach().numpy()

			UpperCAmelCase__	:       Any   =  np.max(np.abs(hf_layer - gluon_layer       )       ).item()
			UpperCAmelCase__	:       List[Any]   =  np.allclose(UpperCamelCase__			,   UpperCamelCase__			,   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:"""			,   UpperCamelCase__       )


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 __future__ import annotations

import math
from collections.abc import Callable


def       UpperCamelCase__(			UpperCamelCase__     :			Callable[[int | float], int | float]					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int = 1_00					,	)->float:
       A__		   =		x_start
       A__		   =		fnc(UpperCamelCase__    )
       A__		   =		0.0

       for _ in range(UpperCamelCase__    ):
              # Approximates curve as a sequence of linear lines and sums their length
              A__		   =		(x_end - x_start) / steps + xa
              A__		   =		fnc(UpperCamelCase__    )
              length += math.hypot(xa - xa					,	fxa - fxa    )

              # Increment step
              A__		   =		xa
              A__		   =		fxa

       return length


if __name__ == "__main__":


       def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]    )->Dict:
              return math.sin(10 * x    )

       print('f(x) = sin(10 * x)')
       print('The length of the curve from x = -10 to x = 10 is:')
       a__: str							   =						10
       while i <= 100_000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10





