

import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
			raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
a_      =     logging.get_logger(__name__)

a_      =     'Hello world! cécé herlolip'

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FairseqRobertaModel.from_pretrained(_a)
	roberta.eval()  # disable dropout
	SCREAMING_SNAKE_CASE :   int					  =							roberta.model.encoder.sentence_encoder
	SCREAMING_SNAKE_CASE :   List[str]					  =							XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings				,      hidden_size=roberta.cfg.model.encoder_embed_dim				,      num_hidden_layers=roberta.cfg.model.encoder_layers				,      num_attention_heads=roberta.cfg.model.encoder_attention_heads				,      intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim				,      max_position_embeddings=514				,      type_vocab_size=1				,      layer_norm_eps=1E-5				,      )
	if classification_head:
		SCREAMING_SNAKE_CASE :   List[str]					  =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"				,      _a)

	SCREAMING_SNAKE_CASE :   Any					  =							XLMRobertaXLForSequenceClassification(_a) if classification_head else XLMRobertaXLForMaskedLM(_a)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_sent_encoder.embed_tokens.weight
	SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_sent_encoder.embed_positions.weight
	SCREAMING_SNAKE_CASE :   Any					  =							torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.

	SCREAMING_SNAKE_CASE :   Any					  =							roberta_sent_encoder.layer_norm.weight
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers):
		# Encoder: start of layer
		SCREAMING_SNAKE_CASE :   BertLayer					  =							model.roberta.encoder.layer[i]
		SCREAMING_SNAKE_CASE :   TransformerSentenceEncoderLayer					  =							roberta_sent_encoder.layers[i]

		SCREAMING_SNAKE_CASE :   RobertaAttention					  =							layer.attention
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn_layer_norm.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.self_attn_layer_norm.bias

		# self attention
		SCREAMING_SNAKE_CASE :   BertSelfAttention					  =							layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size))
		)

		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_layer.self_attn.q_proj.weight
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.self_attn.q_proj.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn.k_proj.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.self_attn.k_proj.bias
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta_layer.self_attn.v_proj.weight
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta_layer.self_attn.v_proj.bias

		# self-attention output
		SCREAMING_SNAKE_CASE :   BertSelfOutput					  =							layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn.out_proj.weight
		SCREAMING_SNAKE_CASE :   List[Any]					  =							roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.final_layer_norm.weight
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.final_layer_norm.bias

		# intermediate
		SCREAMING_SNAKE_CASE :   BertIntermediate					  =							layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.fca.bias

		# output
		SCREAMING_SNAKE_CASE :   BertOutput					  =							layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE :   int					  =							roberta_layer.fca.bias
		# end of layer

	if classification_head:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta.model.classification_heads["mnli"].dense.weight
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta.model.classification_heads["mnli"].dense.bias
		SCREAMING_SNAKE_CASE :   List[Any]					  =							roberta.model.classification_heads["mnli"].out_proj.weight
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta.model.encoder.lm_head.dense.weight
		SCREAMING_SNAKE_CASE :   int					  =							roberta.model.encoder.lm_head.dense.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta.model.encoder.lm_head.layer_norm.weight
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta.model.encoder.lm_head.layer_norm.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta.model.encoder.lm_head.weight
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	SCREAMING_SNAKE_CASE :   torch.Tensor					  =							roberta.encode(_a).unsqueeze(0)  # batch of size 1

	SCREAMING_SNAKE_CASE :   List[str]					  =							model(_a)[0]
	if classification_head:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta.model.classification_heads["mnli"](roberta.extract_features(_a))
	else:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta.model(_a)[0]
	print(our_output.shape				,      their_output.shape)
	SCREAMING_SNAKE_CASE :   str					  =							torch.max(torch.abs(our_output - their_output)).item()
	print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							torch.allclose(_a				,      _a				,      atol=1E-3)
	print("Do both models output the same tensors?"				,      "🔥" if success else "💩")
	if not success:
		raise Exception("Something went wRoNg")

	pathlib.Path(_a).mkdir(parents=_a				,      exist_ok=_a)
	print(f"Saving model to {pytorch_dump_folder_path}")
	model.save_pretrained(_a)


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			a_      =     parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)


from math import pi, sqrt, tan

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

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	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__			(       _a):
	if radius < 0:
		raise ValueError("surface_area_sphere() only accepts non-negative values")
	return 4 * pi * radius**2

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

def 			lowerCamelCase__			(       _a				,      _a):
	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__			(       _a				,      _a				,      _a):
	if radius_a < 0 or radius_a < 0 or height < 0:
		raise ValueError(
		    "surface_area_conical_frustum() only accepts non-negative values")
	SCREAMING_SNAKE_CASE :   Any					  =							(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__			(       _a				,      _a):
	if radius < 0 or height < 0:
		raise ValueError("surface_area_cylinder() only accepts non-negative values")
	return 2 * pi * radius * (height + radius)

def 			lowerCamelCase__			(       _a				,      _a):
	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(_a				,      2) * torus_radius * tube_radius

def 			lowerCamelCase__			(       _a				,      _a):
	if length < 0 or width < 0:
		raise ValueError("area_rectangle() only accepts non-negative values")
	return length * width

def 			lowerCamelCase__			(       _a):
	if side_length < 0:
		raise ValueError("area_square() only accepts non-negative values")
	return side_length**2

def 			lowerCamelCase__			(       _a				,      _a):
	if base < 0 or height < 0:
		raise ValueError("area_triangle() only accepts non-negative values")
	return (base * height) / 2

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	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")
	SCREAMING_SNAKE_CASE :   List[str]					  =							(sidea + sidea + sidea) / 2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							sqrt(
	    semi_perimeter
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea)
	    * (semi_perimeter - sidea))
	return area

def 			lowerCamelCase__			(       _a				,      _a):
	if base < 0 or height < 0:
		raise ValueError("area_parallelogram() only accepts non-negative values")
	return base * height

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	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__			(       _a):
	if radius < 0:
		raise ValueError("area_circle() only accepts non-negative values")
	return pi * radius**2

def 			lowerCamelCase__			(       _a				,      _a):
	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__			(       _a				,      _a):
	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__			(       _a				,      _a):
	if not isinstance(_a				,      _a) 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) = }''')