





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

lowercase_					= """The Nymphenburg Palace is a beautiful palace in Munich!"""





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str    )						->List[str]:
    _SCREAMING_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,
    }

    _SCREAMING_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
    _SCREAMING_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=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
    _SCREAMING_SNAKE_CASE									=			"""openwebtext_ccnews_stories_books_cased"""

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

    _SCREAMING_SNAKE_CASE									=			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__    )
    _SCREAMING_SNAKE_CASE									=			original_bort._collect_params_with_prefix()

    # Build our config 🤗
    _SCREAMING_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(UpperCamelCase__    ),
    }

    _SCREAMING_SNAKE_CASE									=			BertConfig.from_dict(UpperCamelCase__    )
    _SCREAMING_SNAKE_CASE									=			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(__lowerCamelCase :    int    ) -> 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(__lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Dict    ):
        _SCREAMING_SNAKE_CASE									=			hf_param.shape

        _SCREAMING_SNAKE_CASE									=			to_torch(params[gluon_param]    )
        _SCREAMING_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

    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight					,       """word_embed.0.weight"""    )
    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight					,       """encoder.position_weight"""    )
    _SCREAMING_SNAKE_CASE									=			check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias					,       """encoder.layer_norm.beta"""    )
    _SCREAMING_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)
    _SCREAMING_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    ):
        _SCREAMING_SNAKE_CASE									=			hf_bort_model.bert.encoder.layer[i]

        # self attention
        _SCREAMING_SNAKE_CASE									=			layer.attention.self

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

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

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

        # intermediate
        _SCREAMING_SNAKE_CASE									=			layer.intermediate

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

        # output
        _SCREAMING_SNAKE_CASE									=			layer.output

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

    _SCREAMING_SNAKE_CASE									=			tokenizer.encode_plus(UpperCamelCase__    )["""input_ids"""]

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

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

    _SCREAMING_SNAKE_CASE									=			tokenizer.encode_plus(UpperCamelCase__					,       return_tensors="""pt"""    )
    _SCREAMING_SNAKE_CASE									=			hf_bort_model(**UpperCamelCase__    )[0]

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

    _SCREAMING_SNAKE_CASE									=			np.max(np.abs(hf_layer - gluon_layer    )    ).item()
    _SCREAMING_SNAKE_CASE									=			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__":
      lowercase_					= 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."""
      )
      lowercase_					= parser.parse_args()
      convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









from math import pi, sqrt, tan







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if side_length < 0:
								raise ValueError('surface_area_cube() only accepts non-negative values'      )
				return 6 * side_length**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if length < 0 or breadth < 0 or height < 0:
								raise ValueError('surface_area_cuboid() only accepts non-negative values'      )
				return 2 * ((length * breadth) + (breadth * height) + (length * height))







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('surface_area_sphere() only accepts non-negative values'      )
				return 4 * pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('surface_area_hemisphere() only accepts non-negative values'      )
				return 3 * pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0 or height < 0:
								raise ValueError('surface_area_cone() only accepts non-negative values'      )
				return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius_a < 0 or radius_a < 0 or height < 0:
								raise ValueError(
								    'surface_area_conical_frustum() only accepts non-negative values'      )
				__lowerCamelCase		       =    (height**2 + (radius_a - radius_a) ** 2) ** 0.5
				return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0 or height < 0:
								raise ValueError('surface_area_cylinder() only accepts non-negative values'      )
				return 2 * pi * radius * (height + radius)







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if torus_radius < 0 or tube_radius < 0:
								raise ValueError('surface_area_torus() only accepts non-negative values'      )
				if torus_radius < tube_radius:
								raise ValueError(
								    'surface_area_torus() does not support spindle or self intersecting tori'      )
				return 4 * pow(UpperCamelCase__ ,     2      ) * torus_radius * tube_radius







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if length < 0 or width < 0:
								raise ValueError('area_rectangle() only accepts non-negative values'      )
				return length * width







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if side_length < 0:
								raise ValueError('area_square() only accepts non-negative values'      )
				return side_length**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if base < 0 or height < 0:
								raise ValueError('area_triangle() only accepts non-negative values'      )
				return (base * height) / 2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if sidea < 0 or sidea < 0 or sidea < 0:
								raise ValueError('area_triangle_three_sides() only accepts non-negative values'      )
				elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
								raise ValueError('Given three sides do not form a triangle'      )
				__lowerCamelCase		       =    (sidea + sidea + sidea) / 2
				__lowerCamelCase		       =    sqrt(
				    semi_perimeter
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)
				    * (semi_perimeter - sidea)      )
				return area







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if base < 0 or height < 0:
								raise ValueError('area_parallelogram() only accepts non-negative values'      )
				return base * height







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if basea < 0 or basea < 0 or height < 0:
								raise ValueError('area_trapezium() only accepts non-negative values'      )
				return 1 / 2 * (basea + basea) * height







def 			lowerCamelCase_		(       UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius < 0:
								raise ValueError('area_circle() only accepts non-negative values'      )
				return pi * radius**2







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if radius_x < 0 or radius_y < 0:
								raise ValueError('area_ellipse() only accepts non-negative values'      )
				return pi * radius_x * radius_y







def 			lowerCamelCase_		(       UpperCamelCase__				:      float ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if diagonal_a < 0 or diagonal_a < 0:
								raise ValueError('area_rhombus() only accepts non-negative values'      )
				return 1 / 2 * diagonal_a * diagonal_a







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      float      )     ->       float:





				"""simple docstring"""


				if not isinstance(UpperCamelCase__ ,     UpperCamelCase__      ) or sides < 3:
								raise ValueError(
								    'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'      )
				elif length < 0:
								raise ValueError(
								    'area_reg_polygon() only accepts non-negative values as \
length of a side'      )
				return (sides * length**2) / (4 * tan(pi / sides      ))
				return (sides * length**2) / (4 * tan(pi / sides      ))


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

					print("[DEMO] Areas of various geometric shapes: \n")
					print(f'''Rectangle: {area_rectangle(10, 20) = }''')
					print(f'''Square: {area_square(10) = }''')
					print(f'''Triangle: {area_triangle(10, 10) = }''')
					print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
					print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
					print(f'''Rhombus: {area_rhombus(10, 20) = }''')
					print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
					print(f'''Circle: {area_circle(20) = }''')
					print(f'''Ellipse: {area_ellipse(10, 20) = }''')
					print("\nSurface Areas of various geometric shapes: \n")
					print(f'''Cube: {surface_area_cube(20) = }''')
					print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
					print(f'''Sphere: {surface_area_sphere(20) = }''')
					print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
					print(f'''Cone: {surface_area_cone(10, 20) = }''')
					print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
					print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
					print(f'''Torus: {surface_area_torus(20, 10) = }''')
					print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
					print(f'''Square: {area_reg_polygon(4, 10) = }''')
					print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')


